Patent Publication Number: US-2023156030-A1

Title: Asset discovery engine with deep vulnerabilities scanner

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Patent Application No. 63/280,977, titled “ASSET DISCOVERY ENGINE WITH DEEP VULNERABLITIES SCANNER,” and filed on Nov. 18, 2021, the entirety of which is hereby incorporated by reference. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates generally to industrial network security, and more particularly to an asset discovery engine with a deep vulnerabilities scanner with respect to assets in an industrial network. 
     BACKGROUND 
     An industrial network (e.g., an industrial network associated with industrial automation and control systems) often includes thousands of assets such as, for example, sensors, input/output modules, controllers, firewall devices, supervisory nodes, application nodes, and/or other assets. Furthermore, different assets in an industrial network often include different sets of software and/or different sets of hardware connected to the same network or a different network via switches, routers, firewall devices, etc. As such, there are numerous technical challenges related to performing network security management with respect to an industrial network. 
     SUMMARY 
     The details of some embodiments of the subject matter described in this specification are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims. 
     In an embodiment, a system comprises one or more processors, a memory, and one or more programs stored in the memory. The one or more programs comprise instructions configured to receive a request to perform an asset vulnerability assessment of one or more assets within a network. In one or more embodiments, the request comprises an asset descriptor describing the one or more assets. The one or more programs comprise instructions which, when executed by the one or more processors and in response to the request, cause the device to obtain, based on the asset descriptor, aggregated asset property data associated with the one or more assets. The one or more programs also comprise instructions which, when executed by the one or more processors and in response to the request, cause the device to perform the asset vulnerability assessment based on the aggregated asset property data and asset vulnerability signature data stored in an asset vulnerability signature repository. Additionally, the one or more programs comprise instructions which, when executed by the one or more processors and in response to the request, cause the device to perform one or more actions associated with the network in response to determining that the asset vulnerability assessment satisfies a defined criterion. 
     In another embodiment, a method comprises, at a device with one or more processors and a memory, receiving a request to perform an asset vulnerability assessment of one or more assets within a network. In one or more embodiments, the request comprises an asset descriptor describing the one or more assets. In response to the request, the method comprises obtaining, based on the asset descriptor, aggregated asset property data associated with the one or more assets. In response to the request, the method also comprises performing the asset vulnerability assessment based on the aggregated asset property data and asset vulnerability signature data stored in an asset vulnerability signature repository. In response to the request, the method also comprises performing one or more actions associated with the network in response to determining that the asset vulnerability assessment satisfies a defined criterion. 
     In yet another embodiment, a non-transitory computer-readable storage medium comprises one or more programs for execution by one or more processors of a device. The one or more programs comprise instructions which, when executed by the one or more processors, cause the device to receive a request to perform an asset vulnerability assessment of one or more assets within a network. In one or more embodiments, the request comprises an asset descriptor describing the one or more assets. The one or more programs comprise instructions which, when executed by the one or more processors and in response to the request, cause the device to obtain, based on the asset descriptor, aggregated asset property data associated with the one or more assets. The one or more programs comprise instructions which, when executed by the one or more processors and in response to the request, cause the device to perform the asset vulnerability assessment based on the aggregated asset property data and asset vulnerability signature data stored in an asset vulnerability signature repository. The one or more programs comprise instructions which, when executed by the one or more processors and in response to the request, cause the device to perform one or more actions associated with the network in response to determining that the asset vulnerability assessment satisfies a defined criterion. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The description of the illustrative embodiments can be read in conjunction with the accompanying figures. It will be appreciated that for simplicity and clarity of illustration, elements illustrated in the figures have not necessarily been drawn to scale. For example, the dimensions of some of the elements are exaggerated relative to other elements. Embodiments incorporating teachings of the present disclosure are shown and described with respect to the figures presented herein, in which: 
         FIG.  1    illustrates an exemplary networked computing system environment, in accordance with one or more embodiments described herein; 
         FIG.  2    illustrates a schematic block diagram of a framework of an IoT platform of the networked computing system, in accordance with one or more embodiments described herein; 
         FIG.  3    illustrates a system that provides an exemplary environment, in accordance with one or more embodiments described herein; 
         FIG.  4    illustrates another system that provides an exemplary environment, in accordance with one or more embodiments described herein; 
         FIG.  5    illustrates an exemplary computing device, in accordance with one or more embodiments described herein; 
         FIG.  6    illustrates an exemplary system associated with an asset discovery engine, in accordance with one or more embodiments described herein; 
         FIG.  7    illustrates an exemplary data packet, in accordance with one or more embodiments described herein; 
         FIG.  8    illustrates an exemplary system associated with an asset list creator, in accordance with one or more embodiments described herein; 
         FIG.  9    illustrates an exemplary system associated with active directory discovery, in accordance with one or more embodiments described herein; 
         FIG.  10    illustrates an exemplary system associated with guest discovery, in accordance with one or more embodiments described herein; 
         FIG.  11    illustrates an exemplary system associated with an asset list joiner, in accordance with one or more embodiments described herein; 
         FIG.  12    illustrates an exemplary discovery process, in accordance with one or more embodiments described herein; 
         FIG.  13    illustrates an exemplary host discovery, in accordance with one or more embodiments described herein; 
         FIG.  14    illustrates an exemplary port scanner, in accordance with one or more embodiments described herein; 
         FIG.  15    illustrates an exemplary data query, in accordance with one or more embodiments described herein; 
         FIG.  16    illustrates exemplary network signature matching, in accordance with one or more embodiments described herein; 
         FIG.  17    illustrates exemplary host role detection, in accordance with one or more embodiments described herein; 
         FIG.  18    illustrates an exemplary network functionality designation data object, in accordance with one or more embodiments described herein; 
         FIG.  19    illustrates an exemplary flow diagram related to a discovery process, in accordance with one or more embodiments described herein; 
         FIG.  20    illustrates another exemplary flow diagram related to a discovery process, in accordance with one or more embodiments described herein; 
         FIG.  21    illustrates an exemplary flow diagram related to generating an HTML, report, in accordance with one or more embodiments described herein; 
         FIG.  22    illustrates an exemplary electronic interface, in accordance with one or more embodiments described herein; 
         FIG.  23    illustrates an exemplary flow diagram, in accordance with one or more embodiments described herein; 
         FIG.  24    illustrates another exemplary flow diagram, in accordance with one or more embodiments described herein; 
         FIG.  25    illustrates another exemplary flow diagram, in accordance with one or more embodiments described herein; 
         FIG.  26    illustrates another exemplary flow diagram, in accordance with one or more embodiments described herein; 
         FIG.  27    illustrates another exemplary flow diagram, in accordance with one or more embodiments described herein; 
         FIG.  28    illustrates another exemplary flow diagram, in accordance with one or more embodiments described herein; 
         FIG.  29    illustrates another exemplary flow diagram, in accordance with one or more embodiments described herein; 
         FIG.  30    illustrates a flow diagram for generating aggregated asset properties for assets discovered in a network to perform cybersecurity vulnerability assessment of the assets using the aggregated asset properties, in accordance with one or more embodiments described herein; and 
         FIG.  31    illustrates a functional block diagram of a computer that may be configured to execute techniques described in accordance with one or more embodiments described herein. 
     
    
    
     DETAILED DESCRIPTION 
     Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the various described embodiments. However, it will be apparent to one of ordinary skill in the art that the various described embodiments may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments. The term “or” is used herein in both the alternative and conjunctive sense, unless otherwise indicated. The terms “illustrative,” “example,” and “exemplary” are used to be examples with no indication of quality level. Like numbers refer to like elements throughout. 
     The phrases “in an embodiment,” “in one embodiment,” “according to one embodiment,” and the like generally mean that the particular feature, structure, or characteristic following the phrase can be included in at least one embodiment of the present disclosure, and can be included in more than one embodiment of the present disclosure (importantly, such phrases do not necessarily refer to the same embodiment). 
     The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any implementation described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other implementations. 
     If the specification states a component or feature “can,” “may,” “could,” “should,” “would,” “preferably,” “possibly,” “typically,” “optionally,” “for example,” “often,” or “might” (or other such language) be included or have a characteristic, that particular component or feature is not required to be included or to have the characteristic. Such component or feature can be optionally included in some embodiments, or it can be excluded. 
     In general, the present disclosure provides for an “Internet-of-Things” or “IoT” platform for enterprise performance management that uses real-time accurate models and visual analytics to deliver intelligent actionable recommendations for sustained peak performance of an enterprise or organization. The IoT platform is an extensible platform that is portable for deployment in any cloud or data center environment for providing an enterprise-wide, top to bottom view, displaying the status of processes, assets, people, and safety. Further, the IoT platform of the present disclosure supports end-to-end capability to execute digital twins against process data and to translate the output into actionable insights, as detailed in the following description. 
     An industrial network (e.g., an industrial network associated with industrial automation and control systems) often includes thousands of assets such as, for example, sensors, input/output modules, controllers, firewall devices, supervisory nodes, application nodes, and/or other assets. Furthermore, different assets in an industrial network often include different sets of software and/or different sets of hardware connected to the same network or a different network via switches, routers, firewall devices, etc. As such, there are numerous technical challenges related to performing network security management with respect to an industrial network. 
     In an example, an infrastructure of a process industry plant such as a petrochemical plant, an oil and gas refinery, a pharmaceutical plan, a food and beverage plant, a fertilizer plant, a power plant, or another type of industrial plant is generally susceptible to a cyberattack via an industrial automation system and/or a control system of the industrial plant. An industrial automation system and/or a control system of an industrial plant is generally directly or indirectly connected to information technology networks such as a main control room for the industrial plant, a satellite rack room for the industrial plant, a plant network of the industrial plant, the Internet. As such, cyber attackers often exploit an industrial automation system and/or a control system of an industrial plant to take advantage of known and/or newly discovered infrastructure vulnerabilities of the industrial plant. Unlike computers and/or other computing devices implemented via an internet technology network, portions of an industrial automation system and/or a control system of an industrial plant generally include a distributed control system, process controllers, programmable logic controllers, supervisory control and data acquisition systems, computing stations (e.g., consoles, human-machine interfaces, etc.) and/or another type of system configured for process control functionalities with respect to the industrial plant. The portions of the industrial plant associated with process control functionalities is therefore generally susceptible to a cyberattack. 
     In another example, an industrial plant includes assets in different levels (e.g., different zones) such as, for example, assets (e.g., field instrument assets) in level 0 of an industrial network (e.g., zone 0 of the industrial plant), assets (e.g., embedded interface boards and controllers) in level 1 of an industrial network (e.g., zone 1 of the industrial plant), assets (e.g., supervisory nodes) in level 2 of an industrial network (e.g., zone 2 of the industrial plant), and assets (e.g., application nodes) in level 3 of an industrial network (e.g., zone 3 of the industrial plant). In another example, an industrial plant includes respective assets with respective software agents and/or data privileges that generally involve manual intervention (e.g., creating permission for installation of software, copying software, installing software in respective assets, etc.) to update potentially thousands of assets. In another example, an industrial plant includes third-party assets and controllers that include controller backplane assets. As such, the third-party assets may be vulnerable to a cyberattack due to unknown functionality with respect to the controllers. 
     Thus, to address these and/or other issues, an asset discovery engine with a deep vulnerabilities scanner with respect to assets in a network (e.g., an industrial network) is provided. In various embodiments, aggregated asset properties are generated for assets discovered in a network to perform cybersecurity vulnerability assessment of the assets using the aggregated asset properties. In various embodiments, aggregated asset properties are generated by monitoring network traffic broadcasted to the assets and/or based on responses from the assets. In various embodiments, respective risk scores are generated for the assets based on the cybersecurity vulnerability assessment. 
     In one or more embodiments, the asset discovery engine is a smart asset discovery engine is provided to discover, collect and/or analyze data associated with assets in a network. In one or more embodiments, the asset discovery engine is utilized by a web-based application with cloud connectivity to provide asset vulnerability assessment with respect to the assets. In one or more embodiments, the web-based application is deployed in a Level 3 network machine configured to discover active assets in the network, including third-party assets. In one or more embodiments, active assets (e.g., assets from Level 0 to Level 2 in the network) with vulnerabilities are determined in response to discovery of the active assets. Accordingly, with the asset discovery engine disclosed herein, likelihood of a cyberattack with respect to a network (e.g., an industrial network) is reduced. Moreover, with the asset discovery engine disclosed herein, performance of a network (e.g., an industrial network) and/or assets within a network are improved. For instance, by employing one or more techniques disclosed herein, network performance, asset performance and/or process performance is optimized. Additionally, performance of a processing system associated with cybersecurity vulnerability assessment of assets is improved by employing one or more techniques disclosed herein. For example, a number of computing resources, a number of a storage requirements, and/or number of errors associated with cybersecurity vulnerability assessment of assets is reduced by employing one or more techniques disclosed herein. 
       FIG.  1    illustrates an exemplary networked computing system environment  100 , according to the present disclosure. As shown in  FIG.  1   , networked computing system environment  100  is organized into a plurality of layers including a cloud  105  (e.g., cloud layer), a network  110  (e.g., a network layer), and an edge  115  (e.g., edge layer). As detailed further below, components of the edge  115  are in communication with components of the cloud  105  via network  110 . 
     In various embodiments, network  110  is any suitable network or combination of networks and supports any appropriate protocol suitable for communication of data to and from components of the cloud  105  and between various other components in the networked computing system environment  100  (e.g., components of the edge  115 ). According to various embodiments, network  110  includes a public network (e.g., the Internet), a private network (e.g., a network within an organization), or a combination of public and/or private networks. According to various embodiments, network  110  is configured to provide communication between various components depicted in  FIG.  1   . According to various embodiments, network  110  comprises one or more networks that connect devices and/or components in the network layout to allow communication between the devices and/or components. For example, in one or more embodiments, the network  110  is implemented as the Internet, a wireless network, a wired network (e.g., Ethernet), a local area network (LAN), a Wide Area Network (WANs), Bluetooth, Near Field Communication (NFC), or any other type of network that provides communications between one or more components of the network layout. In some embodiments, network  110  is implemented using cellular networks, satellite, licensed radio, or a combination of cellular, satellite, licensed radio, and/or unlicensed radio networks. 
     Components of the cloud  105  include one or more computer systems  120  that form a so-called “Internet-of-Things” or “IoT” platform  125 . It should be appreciated that “IoT platform” is an optional term describing a platform connecting any type of Internet-connected device, and should not be construed as limiting on the types of computing systems useable within IoT platform  125 . In particular, in various embodiments, computer systems  120  includes any type or quantity of one or more processors and one or more data storage devices comprising memory for storing and executing applications or software modules of networked computing system environment  100 . In one embodiment, the processors and data storage devices are embodied in server-class hardware, such as enterprise-level servers. For example, in an embodiment, the processors and data storage devices comprise any type or combination of application servers, communication servers, web servers, super-computing servers, database servers, file servers, mail servers, proxy servers, and/virtual servers. Further, the one or more processors are configured to access the memory and execute processor-readable instructions, which when executed by the processors configures the processors to perform a plurality of functions of the networked computing system environment  100 . 
     Computer systems  120  further include one or more software components of the IoT platform  125 . For example, in one or more embodiments, the software components of computer systems  120  include one or more software modules to communicate with user devices and/or other computing devices through network  110 . For example, in one or more embodiments, the software components include one or more modules  141 , models  142 , engines  143 , databases  144 , services  145 , and/or applications  146 , which may be stored in/by the computer systems  120  (e.g., stored on the memory), as detailed with respect to  FIG.  2    below. According to various embodiments, the one or more processors are configured to utilize the one or more modules  141 , models  142 , engines  143 , databases  144 , services  145 , and/or applications  146  when performing various methods described in this disclosure. 
     Accordingly, in one or more embodiments, computer systems  120  execute a cloud computing platform (e.g., IoT platform  125 ) with scalable resources for computation and/or data storage, and may run one or more applications on the cloud computing platform to perform various computer-implemented methods described in this disclosure. In some embodiments, some of the modules  141 , models  142 , engines  143 , databases  144 , services  145 , and/or applications  146  are combined to form fewer modules, models, engines, databases, services, and/or applications. In some embodiments, some of the modules  141 , models  142 , engines  143 , databases  144 , services  145 , and/or applications  146  are separated into separate, more numerous modules, models, engines, databases, services, and/or applications. In some embodiments, some of the modules  141 , models  142 , engines  143 , databases  144 , services  145 , and/or applications  146  are removed while others are added. 
     The computer systems  120  are configured to receive data from other components (e.g., components of the edge  115 ) of networked computing system environment  100  via network  110 . Computer systems  120  are further configured to utilize the received data to produce a result. According to various embodiments, information indicating the result is transmitted to users via user computing devices over network  110 . In some embodiments, the computer systems  120  is a server system that provides one or more services including providing the information indicating the received data and/or the result(s) to the users. According to various embodiments, computer systems  120  are part of an entity which include any type of company, organization, or institution that implements one or more IoT services. In some examples, the entity is an IoT platform provider. 
     Components of the edge  115  include one or more enterprises  160   a - 160   n  each including one or more edge devices  161   a - 161   n  and one or more edge gateways  162   a - 162   n . For example, a first enterprise  160   a  includes first edge devices  161   a  and first edge gateways  162   a , a second enterprise  160   b  includes second edge devices  161   b  and second edge gateways  162   b , and an nth enterprise  160   n  includes nth edge devices  161   n  and nth edge gateways  162   n . As used herein, enterprises  160   a - 160   n  represent any type of entity, facility, or vehicle, such as, for example, companies, divisions, buildings, manufacturing plants, warehouses, real estate facilities, laboratories, aircraft, spacecraft, automobiles, ships, boats, military vehicles, oil and gas facilities, or any other type of entity, facility, and/or entity that includes any number of local devices. 
     According to various embodiments, the edge devices  161   a - 161   n  represent any of a variety of different types of devices that may be found within the enterprises  160   a - 160   n . Edge devices  161   a - 161   n  are any type of device configured to access network  110 , or be accessed by other devices through network  110 , such as via an edge gateway  162   a - 162   n . According to various embodiments, edge devices  161   a - 161   n  are “IoT devices” which include any type of network-connected (e.g., Internet-connected) device. For example, in one or more embodiments, the edge devices  161   a - 161   n  include assets, sensors, actuators, processors, computers, valves, pumps, ducts, vehicle components, cameras, displays, doors, windows, security components, boilers, chillers, pumps, HVAC components, factory equipment, and/or any other devices that are connected to the network  110  for collecting, sending, and/or receiving information. Each edge device  161   a - 161   n  includes, or is otherwise in communication with, one or more controllers for selectively controlling a respective edge device  161   a - 161   n  and/or for sending/receiving information between the edge devices  161   a - 161   n  and the cloud  105  via network  110 . With reference to  FIG.  2   , in one or more embodiments, the edge  115  include operational technology (OT) systems  163   a - 163   n  and information technology (IT) applications  164   a - 164   n  of each enterprise  160   a - 161   n . The OT systems  163   a - 163   n  include hardware and software for detecting and/or causing a change, through the direct monitoring and/or control of industrial equipment (e.g., edge devices  161   a - 161   n ), assets, processes, and/or events. The IT applications  164   a - 164   n  includes network, storage, and computing resources for the generation, management, storage, and delivery of data throughout and between organizations. 
     The edge gateways  162   a - 162   n  include devices for facilitating communication between the edge devices  161   a - 161   n  and the cloud  105  via network  110 . For example, the edge gateways  162   a - 162   n  include one or more communication interfaces for communicating with the edge devices  161   a - 161   n  and for communicating with the cloud  105  via network  110 . According to various embodiments, the communication interfaces of the edge gateways  162   a - 162   n  include one or more cellular radios, Bluetooth, WiFi, near-field communication radios, Ethernet, or other appropriate communication devices for transmitting and receiving information. According to various embodiments, multiple communication interfaces are included in each gateway  162   a - 162   n  for providing multiple forms of communication between the edge devices  161   a - 161   n , the gateways  162   a - 162   n , and the cloud  105  via network  110 . For example, in one or more embodiments, communication are achieved with the edge devices  161   a - 161   n  and/or the network  110  through wireless communication (e.g., WiFi, radio communication, etc.) and/or a wired data connection (e.g., a universal serial bus, an onboard diagnostic system, etc.) or other communication modes, such as a local area network (LAN), wide area network (WAN) such as the Internet, a telecommunications network, a data network, or any other type of network. 
     According to various embodiments, the edge gateways  162   a - 162   n  also include a processor and memory for storing and executing program instructions to facilitate data processing. For example, in one or more embodiments, the edge gateways  162   a - 162   n  are configured to receive data from the edge devices  161   a - 161   n  and process the data prior to sending the data to the cloud  105 . Accordingly, in one or more embodiments, the edge gateways  162   a - 162   n  include one or more software modules or components for providing data processing services and/or other services or methods of the present disclosure. With reference to  FIG.  2   , each edge gateway  162   a - 162   n  includes edge services  165   a - 165   n  and edge connectors  166   a - 166   n . According to various embodiments, the edge services  165   a - 165   n  include hardware and software components for processing the data from the edge devices  161   a - 161   n . According to various embodiments, the edge connectors  166   a - 166   n  include hardware and software components for facilitating communication between the edge gateway  162   a - 162   n  and the cloud  105  via network  110 , as detailed above. In some cases, any of edge devices  161   a - n , edge connectors  166   a - n , and edge gateways  162   a - n  have their functionality combined, omitted, or separated into any combination of devices. In other words, an edge device and its connector and gateway need not necessarily be discrete devices. 
       FIG.  2    illustrates a schematic block diagram of framework  200  of the IoT platform  125 , according to the present disclosure. The IoT platform  125  of the present disclosure is a platform for enterprise performance management that uses real-time accurate models and visual analytics to deliver intelligent actionable recommendations and/or analytics for sustained peak performance of the enterprise  160   a - 160   n . The IoT platform  125  is an extensible platform that is portable for deployment in any cloud or data center environment for providing an enterprise-wide, top to bottom view, displaying the status of processes, assets, people, and safety. Further, the IoT platform  125  supports end-to-end capability to execute digital twins against process data and to translate the output into actionable insights, using the framework  200 , detailed further below. 
     As shown in  FIG.  2   , the framework  200  of the IoT platform  125  comprises a number of layers including, for example, an IoT layer  205 , an enterprise integration layer  210 , a data pipeline layer  215 , a data insight layer  220 , an application services layer  225 , and an applications layer  230 . The IoT platform  125  also includes a core services layer  235  and an extensible object model (EOM)  250  comprising one or more knowledge graphs  251 . The layers  205 - 235  further include various software components that together form each layer  205 - 235 . For example, in one or more embodiments, each layer  205 - 235  includes one or more of the modules  141 , models  142 , engines  143 , databases  144 , services  145 , applications  146 , or combinations thereof. In some embodiments, the layers  205 - 235  are combined to form fewer layers. In some embodiments, some of the layers  205 - 235  are separated into separate, more numerous layers. In some embodiments, some of the layers  205 - 235  are removed while others may be added. 
     The IoT platform  125  is a model-driven architecture. Thus, the extensible object model  250  communicates with each layer  205 - 230  to contextualize site data of the enterprise  160   a - 160   n  using an extensible graph based object model (or “asset model”). In one or more embodiments, the extensible object model  250  is associated with knowledge graphs  251  where the equipment (e.g., edge devices  161   a - 161   n ) and processes of the enterprise  160   a - 160   n  are modeled. The knowledge graphs  251  of EOM  250  are configured to store the models in a central location. The knowledge graphs  251  define a collection of nodes and links that describe real-world connections that enable smart systems. As used herein, a knowledge graph  251 : (i) describes real-world entities (e.g., edge devices  161   a - 161   n ) and their interrelations organized in a graphical interface; (ii) defines possible classes and relations of entities in a schema; (iii) enables interrelating arbitrary entities with each other; and (iv) covers various topical domains. In other words, the knowledge graphs  251  define large networks of entities (e.g., edge devices  161   a - 161   n ), semantic types of the entities, properties of the entities, and relationships between the entities. Thus, the knowledge graphs  251  describe a network of “things” that are relevant to a specific domain or to an enterprise or organization. Knowledge graphs  251  are not limited to abstract concepts and relations, but can also contain instances of objects, such as, for example, documents and datasets. In some embodiments, the knowledge graphs  251  include resource description framework (RDF) graphs. As used herein, a “RDF graph” is a graph data model that formally describes the semantics, or meaning, of information. The RDF graph also represents metadata (e.g., data that describes data). According to various embodiments, knowledge graphs  251  also include a semantic object model. The semantic object model is a subset of a knowledge graph  251  that defines semantics for the knowledge graph  251 . For example, the semantic object model defines the schema for the knowledge graph  251 . 
     As used herein, EOM  250  includes a collection of application programming interfaces (APIs) that enables seeded semantic object models to be extended. For example, the EOM  250  of the present disclosure enables a customer&#39;s knowledge graph  251  to be built subject to constraints expressed in the customer&#39;s semantic object model. Thus, the knowledge graphs  251  are generated by customers (e.g., enterprises or organizations) to create models of the edge devices  161   a - 161   n  of an enterprise  160   a - 160   n , and the knowledge graphs  251  are input into the EOM  250  for visualizing the models (e.g., the nodes and links). 
     The models describe the assets (e.g., the nodes) of an enterprise (e.g., the edge devices  161   a - 161   n ) and describe the relationship of the assets with other components (e.g., the links). The models also describe the schema (e.g., describe what the data is), and therefore the models are self-validating. For example, in one or more embodiments, the model describes the type of sensors mounted on any given asset (e.g., edge device  161   a - 161   n ) and the type of data that is being sensed by each sensor. According to various embodiments, a KPI framework is used to bind properties of the assets in the extensible object model  250  to inputs of the KPI framework. Accordingly, the IoT platform  125  is an extensible, model-driven end-to-end stack including: two-way model sync and secure data exchange between the edge  115  and the cloud  105 , metadata driven data processing (e.g., rules, calculations, and aggregations), and model driven visualizations and applications. As used herein, “extensible” refers to the ability to extend a data model to include new properties/columns/fields, new classes/tables, and new relations. Thus, the IoT platform  125  is extensible with regards to edge devices  161   a - 161   n  and the applications  146  that handle those devices  161   a - 161   n . For example, when new edge devices  161   a - 161   n  are added to an enterprise  160   a - 160   n  system, the new devices  161   a - 161   n  will automatically appear in the IoT platform  125  so that the corresponding applications  146  understand and use the data from the new devices  161   a - 161   n.    
     In some cases, asset templates are used to facilitate configuration of instances of edge devices  161   a - 161   n  in the model using common structures. An asset template defines the typical properties for the edge devices  161   a - 161   n  of a given enterprise  160   a - 160   n  for a certain type of device. For example, an asset template of a pump includes modeling the pump having inlet and outlet pressures, speed, flow, etc. The templates may also include hierarchical or derived types of edge devices  161   a - 161   n  to accommodate variations of a base type of device  161   a - 161   n . For example, a reciprocating pump is a specialization of a base pump type and would include additional properties in the template. Instances of the edge device  161   a - 161   n  in the model are configured to match the actual, physical devices of the enterprise  160   a - 160   n  using the templates to define expected attributes of the device  161   a - 161   n . Each attribute is configured either as a static value (e.g., capacity is 1000 BPH) or with a reference to a time series tag that provides the value. The knowledge graph  251  can automatically map the tag to the attribute based on naming conventions, parsing, and matching the tag and attribute descriptions and/or by comparing the behavior of the time series data with expected behavior. In one or more embodiments, each of the key attribute contributing to one or more metrics to drive a dashboard is marked with one or more metric tags such that a dashboard visualization is generated. 
     The modeling phase includes an onboarding process for syncing the models between the edge  115  and the cloud  105 . For example, in one or more embodiments, the onboarding process includes a simple onboarding process, a complex onboarding process, and/or a standardized rollout process. The simple onboarding process includes the knowledge graph  251  receiving raw model data from the edge  115  and running context discovery algorithms to generate the model. The context discovery algorithms read the context of the edge naming conventions of the edge devices  161   a - 161   n  and determine what the naming conventions refer to. For example, in one or more embodiments, the knowledge graph  251  receives “TMP” during the modeling phase and determine that “TMP” relates to “temperature.” The generated models are then published. The complex onboarding process includes the knowledge graph  251  receiving the raw model data, receiving point history data, and receiving site survey data. According to various embodiments, the knowledge graph  251  then uses these inputs to run the context discovery algorithms. According to various embodiments, the generated models are edited and then the models are published. The standardized rollout process includes manually defining standard models in the cloud  105  and pushing the models to the edge  115 . 
     The IoT layer  205  includes one or more components for device management, data ingest, and/or command/control of the edge devices  161   a - 161   n . The components of the IoT layer  205  enable data to be ingested into, or otherwise received at, the IoT platform  125  from a variety of sources. For example, in one or more embodiments, data is ingested from the edge devices  161   a - 161   n  through process historians or laboratory information management systems. The IoT layer  205  is in communication with the edge services  165   a - 165   n  installed on the edge gateways  162   a - 162   n  through network  110 , and the edge services  165   a - 165   n  send the data securely to the IoT platform  205 . In some embodiments, only authorized data is sent to the IoT platform  125 , and the IoT platform  125  only accepts data from authorized edge gateways  162   a - 162   n  and/or edge devices  161   a - 161   n . According to various embodiments, data is sent from the edge gateways  162   a - 162   n  to the IoT platform  125  via direct streaming and/or via batch delivery. Further, after any network or system outage, data transfer will resume once communication is re-established and any data missed during the outage will be backfilled from the source system or from a cache of the IoT platform  125 . According to various embodiments, the IoT layer  205  also includes components for accessing time series, alarms and events, and transactional data via a variety of protocols. 
     The enterprise integration layer  210  includes one or more components for events/messaging, file upload, and/or REST/OData. The components of the enterprise integration layer  210  enable the IoT platform  125  to communicate with third party cloud applications  211 , such as any application(s) operated by an enterprise in relation to its edge devices. For example, the enterprise integration layer  210  connects with enterprise databases, such as guest databases, customer databases, financial databases, patient databases, etc. The enterprise integration layer  210  provides a standard application programming interface (API) to third parties for accessing the IoT platform  125 . The enterprise integration layer  210  also enables the IoT platform  125  to communicate with the OT systems  163   a - 163   n  and IT applications  164   a - 164   n  of the enterprise  160   a - 160   n . Thus, the enterprise integration layer  210  enables the IoT platform  125  to receive data from the third-party cloud applications  211  rather than, or in combination with, receiving the data from the edge devices  161   a - 161   n  directly. 
     The data pipeline layer  215  includes one or more components for data cleansing/enriching, data transformation, data calculations/aggregations, and/or API for data streams. Accordingly, in one or more embodiments, the data pipeline layer  215  pre-processes and/or performs initial analytics on the received data. The data pipeline layer  215  executes advanced data cleansing routines including, for example, data correction, mass balance reconciliation, data conditioning, component balancing and simulation to ensure the desired information is used as a basis for further processing. The data pipeline layer  215  also provides advanced and fast computation. For example, cleansed data is run through enterprise-specific digital twins. According to various embodiments, the enterprise-specific digital twins include a reliability advisor containing process models to determine the current operation and the fault models to trigger any early detection and determine an appropriate resolution. According to various embodiments, the digital twins also include an optimization advisor that integrates real-time economic data with real-time process data, selects the right feed for a process, and determines optimal process conditions and product yields. 
     According to various embodiments, the data pipeline layer  215  employs models and templates to define calculations and analytics. Additionally or alternatively, according to various embodiments, the data pipeline layer  215  employs models and templates to define how the calculations and analytics relate to the assets (e.g., the edge devices  161   a - 161   n ). For example, in an embodiment, a pump template defines pump efficiency calculations such that every time a pump is configured, the standard efficiency calculation is automatically executed for the pump. The calculation model defines the various types of calculations, the type of engine that should run the calculations, the input and output parameters, the preprocessing requirement and prerequisites, the schedule, etc. According to various embodiments, the actual calculation or analytic logic is defined in the template or it may be referenced. Thus, according to various embodiments, the calculation model is employed to describe and control the execution of a variety of different process models. According to various embodiments, calculation templates are linked with the asset templates such that when an asset (e.g., edge device  161   a - 161   n ) instance is created, any associated calculation instances are also created with their input and output parameters linked to the appropriate attributes of the asset (e.g., edge device  161   a - 161   n ). 
     According to various embodiments, the IoT platform  125  supports a variety of different analytics models including, for example, first principles models, empirical models, engineered models, user-defined models, machine learning models, built-in functions, and/or any other types of analytics models. Fault models and predictive maintenance models will now be described by way of example, but any type of models may be applicable. 
     Fault models are used to compare current and predicted enterprise  160   a - 160   n  performance to identify issues or opportunities, and the potential causes or drivers of the issues or opportunities. The IoT platform  125  includes rich hierarchical symptom-fault models to identify abnormal conditions and their potential consequences. For example, in one or more embodiments, the IoT platform  125  drill downs from a high-level condition to understand the contributing factors, as well as determining the potential impact a lower level condition may have. There may be multiple fault models for a given enterprise  160   a - 160   n  looking at different aspects such as process, equipment, control, and/or operations. According to various embodiments, each fault model identifies issues and opportunities in their domain, and can also look at the same core problem from a different perspective. According to various embodiments, an overall fault model is layered on top to synthesize the different perspectives from each fault model into an overall assessment of the situation and point to the true root cause. 
     According to various embodiments, when a fault or opportunity is identified, the IoT platform  125  provides recommendations about an optimal corrective action to take. Initially, the recommendations are based on expert knowledge that has been pre-programmed into the system by process and equipment experts. A recommendation services module presents this information in a consistent way regardless of source, and supports workflows to track, close out, and document the recommendation follow-up. According to various embodiments, the recommendation follow-up is employed to improve the overall knowledge of the system over time as existing recommendations are validated (or not) or new cause and effect relationships are learned by users and/or analytics. 
     According to various embodiments, the models are used to accurately predict what will occur before it occurs and interpret the status of the installed base. Thus, the IoT platform  125  enables operators to quickly initiate maintenance measures when irregularities occur. According to various embodiments, the digital twin architecture of the IoT platform  125  employs a variety of modeling techniques. According to various embodiments, the modeling techniques include, for example, rigorous models, fault detection and diagnostics (FDD), descriptive models, predictive maintenance, prescriptive maintenance, process optimization, and/or any other modeling technique. 
     According to various embodiments, the rigorous models are converted from process design simulation. In this manner, process design is integrated with feed conditions and production requirement. Process changes and technology improvement provide business opportunities that enable more effective maintenance schedule and deployment of resources in the context of production needs. The fault detection and diagnostics include generalized rule sets that are specified based on industry experience and domain knowledge and can be easily incorporated and used working together with equipment models. According to various embodiments, the descriptive models identifies a problem and the predictive models determines possible damage levels and maintenance options. According to various embodiments, the descriptive models include models for defining the operating windows for the edge devices  161   a - 161   n.    
     Predictive maintenance includes predictive analytics models developed based on rigorous models and statistic models, such as, for example, principal component analysis (PCA) and partial least square (PLS). According to various embodiments, machine learning methods are applied to train models for fault prediction. According to various embodiments, predictive maintenance leverages FDD-based algorithms to continuously monitor individual control and equipment performance. Predictive modeling is then applied to a selected condition indicator that deteriorates in time. Prescriptive maintenance includes determining an optimal maintenance option and when it should be performed based on actual conditions rather than time-based maintenance schedule. According to various embodiments, prescriptive analysis selects the right solution based on the company&#39;s capital, operational, and/or other requirements. Process optimization is determining optimal conditions via adjusting set-points and schedules. The optimized set-points and schedules can be communicated directly to the underlying controllers, which enables automated closing of the loop from analytics to control. 
     The data insight layer  220  includes one or more components for time series databases (TDSB), relational/document databases, data lakes, blob, files, images, and videos, and/or an API for data query. According to various embodiments, when raw data is received at the IoT platform  125 , the raw data is stored as time series tags or events in warm storage (e.g., in a TSDB) to support interactive queries and to cold storage for archive purposes. According to various embodiments, data is sent to the data lakes for offline analytics development. According to various embodiments, the data pipeline layer  215  accesses the data stored in the databases of the data insight layer  220  to perform analytics, as detailed above. 
     The application services layer  225  includes one or more components for rules engines, workflow/notifications, KPI framework, insights (e.g., actionable insights), decisions, recommendations, machine learning, and/or an API for application services. The application services layer  225  enables building of applications  146   a - d . The applications layer  230  includes one or more applications  146   a - d  of the IoT platform  125 . For example, according to various embodiments, the applications  146   a - d  includes a buildings application  146   a , a plants application  146   b , an aero application  146   c , and other enterprise applications  146   d . According to various embodiments, the applications  146  includes general applications  146  for portfolio management, asset management, autonomous control, and/or any other custom applications. According to various embodiments, portfolio management includes the KPI framework and a flexible user interface (UI) builder. According to various embodiments, asset management includes asset performance and asset health. According to various embodiments, autonomous control includes energy optimization and/or predictive maintenance. As detailed above, according to various embodiments, the general applications  146  is extensible such that each application  146  is configurable for the different types of enterprises  160   a - 160   n  (e.g., buildings application  146   a , plants application  146   b , aero application  146   c , and other enterprise applications  146   d ). 
     The applications layer  230  also enables visualization of performance of the enterprise  160   a - 160   n . For example, dashboards provide a high-level overview with drill downs to support deeper investigations. Recommendation summaries give users prioritized actions to address current or potential issues and opportunities. Data analysis tools support ad hoc data exploration to assist in troubleshooting and process improvement. 
     The core services layer  235  includes one or more services of the IoT platform  125 . According to various embodiments, the core services  235  include data visualization, data analytics tools, security, scaling, and monitoring. According to various embodiments, the core services  235  also include services for tenant provisioning, single login/common portal, self-service admin, UI library/UI tiles, identity/access/entitlements, logging/monitoring, usage metering, API gateway/dev portal, and the IoT platform  125  streams. 
       FIG.  3    illustrates a system  300  that provides an exemplary environment according to one or more described features of one or more embodiments of the disclosure. According to an embodiment, the system  300  includes an asset vulnerability assessment computer system  302  to facilitate a practical application of asset vulnerability assessment with respect to assets within a network. In one or more embodiments, the asset vulnerability assessment computer system  302  facilitates a practical application of monitoring network traffic broadcasted to the assets and/or based on responses from the assets to facilitate asset vulnerability assessment with respect to assets within a network. In one or more embodiments, the asset vulnerability assessment computer system  302  stores and/or analyzes asset property data that is aggregated from one or more assets and/or one or more data sources associated with an enterprise system (e.g., a building system, an industrial system or another type of enterprise system). 
     In an embodiment, the asset vulnerability assessment computer system  302  is a server system (e.g., a server device) that facilitates asset vulnerability assessment with respect to assets within a network. In one or more embodiments, the asset vulnerability assessment computer system  302  is a device with one or more processors and a memory. In one or more embodiments, the asset vulnerability assessment computer system  302  is a computer system from the computer systems  120 . For example, in one or more embodiments, the asset vulnerability assessment computer system  302  is implemented via the cloud  105 . The asset vulnerability assessment computer system  302  is also related to one or more technologies, such as, for example, cybersecurity technologies, asset vulnerability assessment technologies, industrial technologies, process plant technologies, oil and gas technologies, petrochemical technologies, refinery technologies, process plant technologies, supply chain analytics technologies, enterprise technologies, connected building technologies, industrial technologies, Internet of Things (IoT) technologies, data analytics technologies, digital transformation technologies, cloud computing technologies, cloud database technologies, server technologies, network technologies, private enterprise network technologies, wireless communication technologies, machine learning technologies, artificial intelligence technologies, digital processing technologies, electronic device technologies, computer technologies, aircraft technologies, navigation technologies, asset visualization technologies, procurement technologies, and/or one or more other technologies. 
     Moreover, the asset vulnerability assessment computer system  302  provides an improvement to one or more technologies such as cybersecurity technologies, asset vulnerability assessment technologies, industrial technologies, process plant technologies, oil and gas technologies, petrochemical technologies, refinery technologies, process plant technologies, supply chain analytics technologies, enterprise technologies, connected building technologies, industrial technologies, IoT technologies, data analytics technologies, digital transformation technologies, cloud computing technologies, cloud database technologies, server technologies, network technologies, private enterprise network technologies, wireless communication technologies, machine learning technologies, artificial intelligence technologies, digital processing technologies, electronic device technologies, computer technologies, aircraft technologies, navigation technologies, asset visualization technologies, procurement technologies, and/or one or more other technologies. In an implementation, the asset vulnerability assessment computer system  302  improves performance of a computing device. For example, in one or more embodiments, the asset vulnerability assessment computer system  302  improves processing efficiency of a computing device (e.g., a server), reduces power consumption of a computing device (e.g., a server), improves quality of data provided by a computing device (e.g., a server), etc. 
     The asset vulnerability assessment computer system  302  includes a asset discovery component  304 , an asset vulnerability component  306  and/or a action component  308 . Additionally, in one or more embodiments, the asset vulnerability assessment computer system  302  includes a processor  310  and/or a memory  312 . In certain embodiments, one or more aspects of the asset vulnerability assessment computer system  302  (and/or other systems, apparatuses and/or processes disclosed herein) constitute executable instructions embodied within a computer-readable storage medium (e.g., the memory  312 ). For instance, in an embodiment, the memory  312  stores computer executable component and/or executable instructions (e.g., program instructions). Furthermore, the processor  310  facilitates execution of the computer executable components and/or the executable instructions (e.g., the program instructions). In an example embodiment, the processor  310  is configured to execute instructions stored in the memory  312  or otherwise accessible to the processor  310 . 
     The processor  310  is a hardware entity (e.g., physically embodied in circuitry) capable of performing operations according to one or more embodiments of the disclosure. Alternatively, in an embodiment where the processor  310  is embodied as an executor of software instructions, the software instructions configure the processor  310  to perform one or more algorithms and/or operations described herein in response to the software instructions being executed. In an embodiment, the processor  310  is a single core processor, a multi-core processor, multiple processors internal to the asset vulnerability assessment computer system  302 , a remote processor (e.g., a processor implemented on a server), and/or a virtual machine. In certain embodiments, the processor  310  is in communication with the memory  312 , the asset discovery component  304 , the asset vulnerability component  306  and/or the action component  308  via a bus to, for example, facilitate transmission of data among the processor  310 , the memory  312 , the asset discovery component  304 , the asset vulnerability component  306  and/or the action component  308 . The processor  310  may be embodied in a number of different ways and, in certain embodiments, includes one or more processing devices configured to perform independently. Additionally or alternatively, in one or more embodiments, the processor  310  includes one or more processors configured in tandem via a bus to enable independent execution of instructions, pipelining of data, and/or multi-thread execution of instructions. 
     The memory  312  is non-transitory and includes, for example, one or more volatile memories and/or one or more non-volatile memories. In other words, in one or more embodiments, the memory  312  is an electronic storage device (e.g., a computer-readable storage medium). The memory  312  is configured to store information, data, content, one or more applications, one or more instructions, or the like, to enable the asset vulnerability assessment computer system  302  to carry out various functions in accordance with one or more embodiments disclosed herein. As used herein in this disclosure, the term “component,” “system,” and the like, is a computer-related entity. For instance, “a component,” “a system,” and the like disclosed herein is either hardware, software, or a combination of hardware and software. As an example, a component is, but is not limited to, a process executed on a processor, a processor, circuitry, an executable component, a thread of instructions, a program, and/or a computer entity. 
     In an embodiment, the asset vulnerability assessment computer system  302  (e.g., the asset discovery component  304  of the asset vulnerability assessment computer system  302 ) determines asset property data related to the edge devices  161   a - 161   n . In one or more embodiments, the edge devices  161   a - 161   n  are associated with a portfolio of assets. For instance, in one or more embodiments, the edge devices  161   a - 161   n  include one or more assets in a portfolio of assets. The edge devices  161   a - 161   n  include, in one or more embodiments, one or more databases, one or more assets (e.g., one or more industrial assets, one or more building assets, etc.), one or more IoT devices (e.g., one or more industrial IoT devices), one or more connected building assets, one or more sensors, one or more actuators, one or more processors, one or more computers, one or more valves, one or more pumps (e.g., one or more centrifugal pumps, etc.), one or more motors, one or more compressors, one or more turbines, one or more ducts, one or more heaters, one or more chillers, one or more coolers, one or more storage tanks, one or more boilers, one or more furnaces, one or more heat exchangers, one or more fans, one or more blowers, one or more conveyor belts, one or more vehicle components, one or more cameras, one or more displays, one or more security components, one or more HVAC components, industrial equipment, factory equipment, refinery equipment, and/or one or more other devices that are connected to the network  110  for collecting, sending, and/or receiving information. In one or more embodiments, the edge device  161   a - 161   n  include, or is otherwise in communication with, one or more controllers for selectively controlling a respective edge device  161   a - 161   n  and/or for sending/receiving information between the edge devices  161   a - 161   n  and the asset vulnerability assessment computer system  302  via the network  110 . The asset property data includes, for example, an internet protocol (IP) address, a media access control (MAC) address, a hostname, a manufacturer, an operating system, a transmission control protocol (TCP) port, a user datagram protocol (UDP) port, a service, a role, metadata, flow flags, port status (e.g., open state, closed state, etc.), asset state, asset type, asset discovery information, and/or other information associated with one or more edge devices from the edge devices  161   a - 161   n . In one or more embodiments, the asset property data is additionally or alternatively associated with one or more asset processes related to one or more edge devices from the edge devices  161   a - 161   n . For example, in one or more embodiments, the asset property data additionally or alternatively includes data generated by one or more asset processes, data generated by one or more asset processes, and/or other data related to one or more asset processes. 
     In one or more embodiments, the asset vulnerability assessment computer system  302  (e.g., the asset discovery component  304  of the asset vulnerability assessment computer system  302 ) is in communication with the edge devices  161   a - 161   n  via the network  110 . In one or more embodiments, the network  110  is a Wi-Fi network, a Near Field Communications (NFC) network, a Worldwide Interoperability for Microwave Access (WiMAX) network, a personal area network (PAN), a short-range wireless network (e.g., a Bluetooth® network), an infrared wireless (e.g., IrDA) network, an ultra-wideband (UWB) network, an induction wireless transmission network, and/or another type of network. In one or more embodiments, the edge devices  161   a - 161   n  are associated with an industrial environment (e.g., a plant, etc.). Additionally or alternatively, in one or more embodiments, the edge devices  161   a - 161   n  are associated with components of the edge  115  such as, for example, one or more enterprises  160   a - 160   n.    
     The asset discovery component  304  is configured for asset discovery to detect one or more assets (e.g., the edge devices  161   a - 161   n ) within the network  110 . For instance, the asset discovery component  304  is configured to perform one or more asset discovery processes associated with the network  110  to detect the one or more assets (e.g., the edge devices  161   a - 161   n ) within the network  110 . In an embodiment, the asset discovery component  304  detects one or more assets within a certain IP range of the network  110 . In one or more embodiments, the asset discovery component  304  also aggregates asset property data related to the one or more assets (within the network  110  to provide aggregated asset property data. For instance, in one or more embodiments, the asset discovery component  304  aggregates the asset property data into an asset property database  318  to store the aggregated asset property data. In an embodiment, the asset discovery component  304  performs the one or more asset discovery processes in response to an action (e.g., a user-initiated action, modification of an interactive graphical element, etc.) initiated via an electronic interface of a computing device associated with a user. In another embodiment, the asset discovery component  304  performs the one or more asset discovery processes in response to an action initiated in response to a timer (e.g., an asset schedule) satisfying a defined timer threshold value. For example, in certain embodiments, the asset discovery component  304  performs the one or more asset discovery processes based on a schedule (e.g., every 30 minutes, every hour, one or more times per day, one or more times per week, etc.). In one or more embodiments, the asset discovery component  304  performs the one or more asset discovery processes based on one or more performance requirements associated with the one or more assets within the network  110 . For example, in one or more embodiments, the asset discovery component  304  performs the one or more asset discovery processes based on CPU requirements, memory requirements, a firmware version, and/or other performance requirements associated with the one or more assets within the network  110 . 
     In one or more embodiments, the asset discovery component  304  performs the one or more asset discovery processes with respect to one or more assets in a Level 0 (e.g., zone 0) of the network  110 , a Level 1 (e.g., zone 1) of the network  110 , and/or a Level 2 (e.g., zone 2) of the network  110 . In one or more embodiments, a Level 0 (e.g., zone 0) of the network  110  includes field instrumentation assets associated with technical parameters such as, for example, firmware version/revision, tag name, sensor type, status, manufacturer, communication type, and/or other information. Additionally, a Level 1 (e.g., zone 1) of the network  110  includes embedded IOM&#39;s and controllers associated with parameters such as, for example, firmware version/revision, module type, synchronization status, manufacturer, module status, performance data (e.g., CPU performance, memory performance, etc.), and/or other information. Additionally, a Level 1 (e.g., zone 1) of the network  110  includes supervisory/application/network switches nodes asset report associated with parameters such as, for example, model, type, manufacturer, IOS type, mirror port information, up/down ports, memory, CPU, operating system, open TCP, UDP ports, and/or other information. 
     In one or more embodiments, the asset discovery component  304  generates the aggregated asset property data by monitoring network traffic broadcasted to the one or more assets. Additionally or alternatively, the asset discovery component  304  generates the aggregated asset property data based on one or more communications broadcasted by the one or more assets. In one or more embodiments, the asset discovery component  304  generates the aggregated asset property data by scanning, based on one or more data acquisition protocols associated with the one or more assets, one or more ports (e.g., one or more TCP ports, one or more UDP ports, etc.) of the one or more assets. 
     In one or more embodiments, the asset vulnerability assessment computer system  302  (e.g., the asset vulnerability component  306  of the asset vulnerability assessment computer system  302 ) receives a request  320  to perform an asset vulnerability assessment of the one or more assets within the network  110 . In one or more embodiments, the request  320  includes one or more asset descriptors that describe the one or more assets. For instance, in one or more embodiments, the request  320  includes one or more asset descriptors that describe the edge devices  161   a - 161   n . An asset descriptor includes, for example, an asset name, an asset identifier, an asset level, an IP address, a MAC address, a hostname, a manufacturer, an operating system identifier, a TCP port identifier, a UDP port identifier, a service identifier, a role identifier, metadata, an asset state, asset discovery information, a sensor identifier, and/or other information associated with the one or more assets. In one or more embodiments, the request  320  includes a request to generate a dashboard visualization associated with the asset vulnerability assessment. In one or more embodiments, the request  320  is received in response to an action (e.g., a user-initiated action, modification of an interactive graphical element, etc.) initiated via an electronic interface of a computing device. In one or more embodiments, the request  320  is received in response to an action initiated via a processing unit (e.g., an edge device, a controller, etc.) associated with the one or more assets. In one or more embodiments, the request  320  is received in response to an asset schedule satisfying a defined criterion (e.g., an asset schedule interval being above a threshold timer). In one or more embodiments, the request  320  is received in response to detection of a possible anomaly associated with an asset (e.g., in response to an anomaly index for an asset being greater than a specified threshold level). Additionally or alternatively, in one or more embodiments, the request  320  includes one or more user identifiers describing a user role for a user associated with access of a dashboard visualization. A user identifier includes, for example, an identifier for a user role name (e.g., a manager, an executive, a maintenance engineer, a process engineer, etc.). 
     In response to the request  320 , the asset vulnerability component  306  obtains aggregated asset property data associated with the one or more assets. For example, in one or more embodiments, the asset vulnerability component  306  obtains the aggregated asset property data from the asset property database  318 . The aggregated asset property data includes, for example, aggregated IP addresses, aggregated MAC addresses, aggregated hostname data, aggregated manufacturer data, aggregated operating system data, aggregated TCP port data, aggregated UDP port data, aggregated service data, aggregated role data, aggregated metadata, aggregated flow flags data, aggregated port status data (e.g., aggregated open state data, aggregated closed state data, etc.), aggregated asset state data, aggregated asset type data, aggregated asset discovery information, and/or other aggregated information associated with one or more edge devices from the edge devices  161   a - 161   n    
     Additionally, the asset vulnerability component  306  performs the asset vulnerability assessment based on the aggregated asset property data and asset vulnerability signature data stored in an asset vulnerability signature repository. For instance, the asset vulnerability component  306  determines whether the asset vulnerability component  306  satisfies a defined criterion based on a comparison between the aggregated asset property data and asset vulnerability signature data. The asset vulnerability signature data includes, for example, one or more data signatures (e.g., one or more digital patterns, one or more digital fingerprints, etc.) that correspond to one or more asset vulnerability events and/or one or more asset vulnerability patterns. In an embodiment, at least a portion of the asset vulnerability signature data is stored in the asset property database  318 . Additionally or alternatively, at least a portion of the asset vulnerability signature data is stored in an asset vulnerability signature repository associated with the asset property database  318  and/or another database (e.g., the memory  312 , etc.). In one or more embodiments, the asset vulnerability component  306  compares one or more portions of the aggregated asset property data to one or more portions of the asset vulnerability signature data. In response to a match, the asset vulnerability component  306  can determine that a vulnerability exists for an asset. 
     In one or more embodiments, in response to determining that the asset vulnerability assessment satisfies a defined criterion (e.g., in response to a match), the action component  308  performs one or more actions associated with the network  110 . For instance, in response to determining that the asset vulnerability assessment satisfies a defined criterion (e.g., in response to a match), the action component  308  generates action data  322  to facilitate the one or more actions associated with the network  110 . 
     In one or more embodiments, the asset vulnerability component  306  determines respective risk scores for the assets based on the cybersecurity vulnerability assessment. Furthermore, based on a risk score, the action component  308  performs a predetermined action such as a predetermined action associated with security control. In an embodiment, an action includes altering an administrative control with respect to an asset, altering a policy with respect to an asset, alter a configuration setting with respect to an asset, disconnecting a physical hardware device associated with an asset from a network, increasing a degree of monitoring with respect to an asset, altering strength of authentication with respect to an asset, altering a communication channel associated with an asset, and/or one or more other types of actions with respect to an asset. 
     In an embodiment, an action includes generating a report associated with the asset vulnerability assessment. In another embodiment, an action includes generating a user-interactive electronic interface that renders a visual representation of data associated with the o asset vulnerability assessment. In another embodiment, an action includes disconnecting one or more assets from the network  110 . In another embodiment, an action from the one or more actions includes providing one or more recommended actions for one or more assets based on the asset vulnerability assessment. In another embodiment, an action from the one or more actions includes transmitting, to a computing device, one or more notifications associated with the asset vulnerability assessment. In another embodiment, an action from the one or more actions includes altering one or more portions of the network  110  based on the asset vulnerability assessment. In another embodiment, an action from the one or more actions includes providing an optimal process condition for an asset based on the asset vulnerability assessment. For example, in another embodiment, an action from the one or more actions includes adjusting a set-point and/or a schedule for an asset based on the asset vulnerability assessment. In another embodiment, an action from the one or more actions includes one or more corrective actions to take for an asset based on the asset vulnerability assessment. In another embodiment, an action from the one or more actions includes providing an optimal maintenance option for an asset based on the asset vulnerability assessment. In another embodiment, an action from the one or more actions includes an action associated with the application services layer  225 , the applications layer  230 , and/or the core services layer  235  based on the asset vulnerability assessment. In certain embodiments, the action component  308  provides a dashboard visualization to an electronic interface of a computing device based on the asset vulnerability assessment. In one or more embodiments, the dashboard visualization includes data associated with the asset vulnerability assessment. In one or more embodiments, the dashboard visualization includes one or more metrics associated with the asset vulnerability assessment. In certain embodiments, an action from the one or more actions includes configuring the dashboard visualization (e.g., based on the asset vulnerability assessment) to provide individual control of the one or more assets via the dashboard visualization. In certain embodiments, an action from the one or more actions includes configuring the dashboard visualization (e.g., based on the asset vulnerability assessment) to facilitate creation of one or more work orders for the one or more assets. 
       FIG.  4    illustrates a system  300 ′ that provides an exemplary environment according to one or more described features of one or more embodiments of the disclosure. In an embodiment, the system  300 ′ corresponds to an alternate embodiment of the system  400  shown in  FIG.  4   . According to an embodiment, the system  300 ′ includes the asset vulnerability assessment computer system  302 , the edge devices  161   a - 161   n , the asset property database  318  and/or a computing device  402 . In one or more embodiments, the asset vulnerability assessment computer system  302  is in communication with the edge devices  161   a - 161   n  and/or the computing device  402  via the network  110 . The computing device  402  is a mobile computing device, a smartphone, a tablet computer, a mobile computer, a desktop computer, a laptop computer, a workstation computer, a wearable device, a virtual reality device, an augmented reality device, or another type of computing device located remote from the asset vulnerability assessment computer system  302 . 
     In one or more embodiments, the action component  308  communicates one or more portions of the action data  322  to the computing device  402 . For example, in one or more embodiments, the action data  322  includes one or more visual elements for a visual display (e.g., a user-interactive electronic interface) of the computing device  402  that renders a visual representation of the data associated with the asset vulnerability assessment. In certain embodiments, the visual display of the computing device  402  displays one or more graphical elements associated with the action data  322  (e.g., the data associated with the asset vulnerability assessment). In another example, in one or more embodiments, the action data  322  includes one or notifications associated with the asset vulnerability assessment. In one or more embodiments, the action data  322  allows a user associated with the computing device  402  to make decisions and/or perform one or more actions with respect to the asset vulnerability assessment. In one or more embodiments, the action data  322  allows a user associated with the computing device  402  to control the one or more portions of the one or more assets (e.g., one or more portions of the edge devices  161   a - 161   n ). In one or more embodiments, the action data  322  allows a user associated with the computing device  402  to generate one or more work orders for the one or more assets. In one or more embodiments, the action data  322  provides a report associated with the asset vulnerability assessment via a display of the computing device  402 . 
       FIG.  5    illustrates a system  500  according to one or more embodiments of the disclosure. The system  500  includes the computing device  402 . In one or more embodiments, the computing device  402  employs mobile computing, augmented reality, cloud-based computing, IoT technology and/or one or more other technologies to provide performance data, video, audio, text, graphs, charts, real-time data, graphical data, one or more communications, one or more messages, one or more notifications, and/or other media data associated with the one or more metrics. The computing device  402  includes mechanical components, electrical components, hardware components and/or software components to facilitate determining prioritized actions and/or one or more metrics associated with the asset data  314 . In the embodiment shown in  FIG.  5   , the computing device  402  includes a visual display  504 , one or more speakers  506 , one or more cameras  508 , one or more microphones  510 , a global positioning system (GPS) device  512 , a gyroscope  514 , one or more wireless communication devices  516 , and/or a power supply  518 . 
     In an embodiment, the visual display  504  is a display that facilitates presentation and/or interaction with one or more portions of the action data  322 . In one or more embodiments, the computing device  402  displays an electronic interface (e.g., a graphical user interface) associated with an asset performance management platform. In one or more embodiments, the visual display  504  is a visual display that renders one or more interactive media elements via a set of pixels. The one or more speakers  506  include one or more integrated speakers that project audio. The one or more cameras  508  include one or more cameras that employ autofocus and/or image stabilization for photo capture and/or real-time video. The one or more microphones  510  include one or more digital microphones that employ active noise cancellation to capture audio data. The GPS device  512  provides a geographic location for the computing device  402 . The gyroscope  514  provides an orientation for the computing device  402 . The one or more wireless communication devices  516  includes one or more hardware components to provide wireless communication via one or more wireless networking technologies and/or one or more short-wavelength wireless technologies. The power supply  518  is, for example, a power supply and/or a rechargeable battery that provides power to the visual display  504 , the one or more speakers  506 , the one or more cameras  508 , the one or more microphones  510 , the GPS device  512 , the gyroscope  514 , and/or the one or more wireless communication devices  516 . In certain embodiments, the action data  322  associated with the prioritized actions and/or the one or more metrics is presented via the visual display  504  and/or the one or more speakers  506 . 
       FIG.  6    illustrates a system  600  according to one or more embodiments of the disclosure. The system  600  includes an asset discovery engine  602 . The asset discovery engine  602  is, for example, an asset discovery engine of the asset discovery component  304 . In one or more embodiments, the asset discovery engine  602  is configured to detect one or more assets in the network  110  such as, for example, one or more supplemental assets  604 , one or more network device assets  606 , one or more controller assets  608  (e.g., one or more third-party controller assets), one or more backup and restore assets  610 , one or more antivirus assets  612 , one or more domain controller assets  614 , one or more process history database (PHD) assets  616 , and/or one or more server/station assets  618 . In one or more embodiments, the asset discovery engine  602  is configured to discover one or more assets within a Level 3 (e.g., zone 2) of the network  110 . In one or more embodiments, the asset discovery engine  602  is configured to discover the one or more networks via an application programming interface associated with an asset, a management instrumentation associated with an asset, a network management protocol associated with an asset, an application protocol associated with an asset, a network port associated with an asset, a transmission control protocol associated with an asset, and/or another network component associated with an asset. In one or more embodiments, based on the one or more assets discovered by the asset discovery engine  602 , the asset discovery engine  602  determines asset property data  620 . The asset property data  620  includes information such as, for example, availability, configured services, CPU information, storage information, installed software information, log analyzer information, data transfer information, virus scan information, operation system information, process information, memory information, update checker information, update summary information, ping result information, configuration information, error count, logic information, packet rate, system information, system description, utilization information, storage location, total free space, event number, threat event information, machine name, user name, activation information, disablement information, interface information, process state, query information, and/or other information. 
       FIG.  7    illustrates a data packet  700  according to one or more embodiments of the disclosure. In one or more embodiments, the asset discovery engine  602  determine at least a portion of the asset property data  620  by monitoring and/or analyzing the data packet  700 . In an embodiment, the data packet  700  includes a header  704 , a set of parameters  706 , parameter data  708 , and/or a data block  710 . In certain embodiments, the data packet  700  is configured as an S7 telegram associated with an S7 protocol. In certain embodiments, the header  704 , the set of parameters  706 , the parameter data  708 , and/or the data block  710  are encoded within a protocol data unit (PDU) data block  712  of a TCP data packet. In certain embodiments, the header  704 , the set of parameters  706 , the parameter data  708 , and/or the data block  710  are additionally or alternatively encoded within an ISO TCP data block  712  of a TCP/IP data packet. 
       FIG.  8    illustrates a system  800  according to one or more embodiments of the disclosure. The system  800  includes an asset list creator  802 . In one or more embodiments, the asset discovery engine  602  and/or the asset discovery component  304  includes the asset list creator  802 . The asset list creator  802  is configured to convert an IP ranges list  804  into an asset list  806 . For example, in one or more embodiments, the asset discovery engine  602  (e.g., the asset discovery component  304 ) employs the IP ranges list  804  to scan one or more IP ranges of the network  110  and to discover one or more assets in the one or more IP ranges. The asset list  806  includes, for example, the one or more assets discovered in the one or more IP ranges. In one or more embodiments, at least a portion of the IP ranges list  804  is provided by a user (e.g., via the electronic interface associated with the visual display  504  of the computing device  402 ). 
       FIG.  9    illustrates a system  900  according to one or more embodiments of the disclosure. The system  900  includes active directory discovery  902 . In one or more embodiments, the asset discovery engine  602  and/or the asset discovery component  304  includes the active directory discovery  902 . The active directory discovery  902  is configured to determine information associated with assets using an active directory protocol and/or a lightweight directory access protocol. For example, in one or more embodiments, the active directory discovery  902  employs an IP address  904 , a base  906 , a username  908 , a password  910 , and/or a port  912  to determines an assets list  914 . The assets list  914  includes, in certain embodiments, a hostname, an IP address, an operating system, and/or other information associated with respective assets. 
       FIG.  10    illustrates a system  1000  according to one or more embodiments of the disclosure. The system  1000  includes guest discovery  1002 . In one or more embodiments, the asset discovery engine  602  and/or the asset discovery component  304  includes the guest discovery  1002 . The guest discovery  1002  is configured to determine information associated with assets using a centralized server. For example, in one or more embodiments, the guest discovery  1002  employs a username  1004 , a password  1006 , and/or a server IP  1008  to determines an assets list  1014 . The assets list  1014  includes, in certain embodiments, a hostname, an IP address, an operating system, and/or other information associated with respective assets. 
       FIG.  11    illustrates a system  1100  according to one or more embodiments of the disclosure. The system  1100  includes an asset list joiner  1102 . In one or more embodiments, the asset discovery engine  602  and/or the asset discovery component  304  includes the asset list joiner  1102 . In one or more embodiments, the asset list joiner  1102  aggregates asset information (e.g., assets lists, asset property data, etc.) from the asset list creator  802 , the active directory discovery  902 , the guest discovery  1002 , and/or a network query  1104 . In an embodiment, the network query  1104  corresponds to a query of the network  110  to discover one or more assets. 
     In one or more embodiments, the asset list joiner  1102  acquires lists of assets and combines lists of assets into an aggregated list of assets. In an embodiment, the asset list joiner  1102  combines lists of assets into an aggregated list of assets based on a unique identifier for respective assets. In an embodiment, the unique identifier corresponds to at least a portion of an IP address and/or a MAC address of an asset. In certain embodiments where multiple assets include a corresponding unique identifier, the asset list joiner  1102  combines the respective asset information into a single entry for the aggregated list of assets. In an exemplary embodiment, an aggregated list of assets corresponds to the following data structure: 
     
       
         
           
               
               
               
               
               
               
               
               
               
             
               
                   
                   
               
               
                   
                   
                 Active 
                   
                   
                   
                   
                 Network 
                   
               
               
                   
                 Data 
                 Directory 
                 Guest 
                 Host 
                 Port 
                 Data 
                 Signature 
                 Role 
               
               
                   
                 Type 
                 Discovery 
                 Discovery 
                 Discovery 
                 Scanner 
                 Query 
                 Matching 
                 Detector 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
            
               
                 Hostname 
                 String 
                 ✓ 
                 ✓ 
                   
                   
                 ✓ 
                   
                   
               
               
                 Vendor 
                 String 
                   
                   
                 ✓ 
               
               
                 IP 
                 String 
                 ✓ 
                 ✓ 
                 ✓ 
               
               
                 MAC 
                 String 
                   
                 ✓ 
                 ✓ 
               
               
                 TCP 
                 Set 
                   
                   
                   
                 ✓ 
               
               
                 UDP 
                 Set 
                   
                   
                   
                 ✓ 
               
               
                 OS 
                 String 
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                 ✓ 
                   
                   
                 ✓ 
                 ✓ 
               
               
                 Services 
                 Set 
                   
                   
                   
                   
                 ✓ 
               
               
                 Role 
                 String 
                   
                   
                   
                   
                   
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       FIG.  12    illustrates a discovery process  1200  according to one or more embodiments of the disclosure. The discovery process  1200  is performed, for example, by the asset discovery engine  602  and/or the asset discovery component  304 . The discovery process  1200  includes host discovery  1202 , port scanner  1204 , data query  1206 , network signature matching  1208 , and/or host role detector  1210 . In one or more embodiments, the host discovery  1202 , the port scanner  1204 , the data query  1206 , the network signature matching  1208 , and/or the host role detector  1210  are performed to facilitate acquiring asset property data for the respective assets. A host is, for example, an asset within the network  110 . 
       FIG.  13    illustrates the host discovery  1202  according to one or more embodiments of the disclosure. In one or more embodiments, the asset discovery engine  602  and/or the asset discovery component  304  includes the host discovery  1202 . In one or more embodiments, the host discovery  1202  is configured to detect whether a given IP represents an active host (e.g., an asset within the network  110 ). For example, in one or more embodiments, the host discovery  1202  receives an IP address  1302  and, based on the IP address  1302 , the host discovery  1202  determines whether the host is active (e.g., host is up/down  1304 ) and/or determines an IP address of the host (e.g., host IP address  1306 ). In certain embodiments, if the IP address  1302  is determined to be a part of a network for a server (e.g., a LAN of the server), the host discovery  1202  additionally determines a MAC address of the host (e.g., host MAC address  1308 ). In one or more embodiments, the host discovery  1202  employs one or more host discovery techniques such as, for example, ICMP ping, TCP SYN discovery, TCP ACK discovery, ICMP timestamp ping, ARP ping, and/or another type of host discovery technique. 
       FIG.  14    illustrates the port scanner  1204  according to one or more embodiments of the disclosure. In one or more embodiments, the asset discovery engine  602  and/or the asset discovery component  304  includes the port scanner  1204 . In one or more embodiments, the port scanner  1204  is configured to examine a port state (e.g., a TCP port state and/or a UDP port state) of an asset (e.g., an asset within the network  110 ). In one or more embodiments, the port scanner  1204  receives asset information  1402 , a TCP ports list  1404 , and/or a UDP ports list  1406 . In an embodiment, the asset information  1402  is provided by the host discovery  1202 . In response to the asset information  1402 , the TCP ports list  1404 , and/or the UDP ports list  1406 , the port scanner  1204  provides asset information  1408  related to assets with open ports. 
       FIG.  15    illustrates the data query  1206  according to one or more embodiments of the disclosure. In one or more embodiments, the asset discovery engine  602  and/or the asset discovery component  304  includes the data query  1206 . In one or more embodiments, the data query  1206  employs connection-based protocols (e.g., WMI, SSH, etc.) and/or connection-less protocols (e.g., SNMP, NBNS, etc.) to remotely query one or more assets for asset property data  1504  such as, for example, an IP address, a MAC address, a hostname, a manufacturer, an operating system, a TCP port, a UDP port, a service, a role, metadata, flow flags, port status (e.g., open state, closed state, etc.), asset type, asset state, asset discovery information, and/or other information associated with the one or more assets. The data query  1206  determines the one or assets for the query, for example, based on the asset information  1408 . In certain embodiments, the data query  1206  performs the query with respect to the one or more assets in response to an authentication process associated with a user. For example, in certain embodiments, the data query  1206  employs credentials  1502  associated with a user to initiate the query with respect to the one or more assets. In one or more embodiments, the data query  1206  executes connection attempts using a specific protocol and/or based on open ports identified by the port scanner  1204 . In one or more embodiments, the data query  1206  performs the query with respect to the one or more assets based on information related to one or more services executed via the one or more assets and/or one or more computing devices in communication with the one or more assets. 
       FIG.  16    illustrates the network signature matching  1208  according to one or more embodiments of the disclosure. In one or more embodiments, the asset discovery engine  602 , the asset discovery component  304 , and/or the asset vulnerability component  306  includes the network signature matching  1208 . In one or more embodiments, the network signature matching  1208  is configured to process one or more assets that did not pass the data querying associated with the data query  1206 . For example, in one or more embodiments, the network signature matching  1208  is configured to process one or more assets in which the data query  1206  was not able to connect to and/or query to obtain meaning the data query step was unable to connect to and/or was unable to query for asset property data (e.g., due to reasons such as wrong credentials, a connection protocol being not supported, etc.). In one or more embodiments, the network signature matching  1208  employs asset information  1602  associated with the one or more assets that did not pass the data querying to determine asset property data  1604  such as, for example, an IP address, a MAC address, a hostname, a manufacturer, an operating system, a TCP port, a UDP port, a service, a role, metadata, flow flags, port status (e.g., open state, closed state, etc.), asset type, asset state, asset discovery information, and/or other information associated with the one or more assets. Additionally or alternatively, in one or more embodiments, the network signature matching  1208  examines other characteristics related to network communication provided by and/or received by one or more assets in the network  110  to determine at least a portion of the asset property data  1604 . For example, in an embodiment, the network signature matching  1208  analyzes a TTL parameter of a TCP packet transmitted and/or received by an asset. In one or more embodiments, the network signature matching  1208  performs one or more fingerprinting techniques (e.g., one or more operation system fingerprinting techniques) that determines a data fingerprint for an asset (e.g., a data fingerprint associated with at least a portion of the asset property data  1604 ) and compares the data fingerprint against a repository of predetermined fingerprints. 
       FIG.  17    illustrates the host role detector  1210  according to one or more embodiments of the disclosure. In one or more embodiments, the asset discovery engine  602 , the asset discovery component  304 , and/or the asset vulnerability component  306  includes the host role detector  1210 . In one or more embodiments, the host role detector  1210  analyzes gathered data related to one or more assets via a series of heuristic tests to determine a role or roles associated with respective assets. In one or more embodiments, the host role detector  1210  compares signatures of predetermined roles with the respective signatures for respective assets to determine one or more roles and/or one or more services provided by the respective assets. For example, in one or more embodiments, the host role detector  1210  employs asset property data  1702  associated with one or more assets to determine asset role data  1704  associated with one or more roles and/or one or more services provided by the one or more assets. The asset property data  1702  includes information such as, for example, an IP address, a MAC address, a hostname, a manufacturer, an operating system, a TCP port, a UDP port, a service, a role, metadata, flow flags, port status (e.g., open state, closed state, etc.), asset type, asset state, asset discovery information, and/or other information associated with the one or more asset. In one or more embodiments, the host role detector  1210  determines a main role and/or a main service provided by an asset by applying a weight to respective roles and/or respective services of the asset and selecting a highest weight. 
     An operational example of a network functionality designation data object  1800  is depicted in  FIG.  18   . As depicted in  FIG.  18   , the network functionality designation data object  1800  provides (in addition to links between the depicted network assets) a network functionality designation for each network asset. For example, as depicted in the network functionality designation data object  1800  of  FIG.  8   , the network asset  1801  is a host, the network asset  1802  is a switch, the network asset  1803  is a router, and network assets  1804  and  1805  are undetected-type networking devices. In one or more embodiments, the network asset is a controller, a switch, a router, a network element, a host, a computing machine, a computing device, a printer, an AB, a hub, and/or an edge device (e.g., edge device(s)  161   a - 161   n ). 
       FIG.  19    illustrates an exemplary flow diagram  1900  related to the discovery process  1200  according to one or more embodiments of the disclosure. In an embodiment host discovery is executed at step  1902 . In response to executing the host discovery, a host discovery function is run on an asset at step  1904 . In some embodiments, a step  1906  is performed to determine whether an asset is active. In response to performing step  1904  and/or step  1906 , the port scanner is executed at step  1908 . In response to executing the port scanner, a port scanner function is run on an asset at step  1910 . Additionally or alternatively, in response to performing step  1904  and/or step  1906 , the data query is executed at step  1912 . In response to executing the data query, a data query function is run on an asset at step  1914 . Additionally or alternatively, in response to performing step  1904  and/or step  1906 , the network signature matching is executed at step  1916 . In response to executing the data signature matching, a network signature matching function is run on an asset at step  1918 . Additionally or alternatively, in response to performing step  1904  and/or step  1906 , the role detector is executed at step  1920 . In response to executing the role detector, a role detector function is run on an asset at step  1922 . In response to step  1908 , step  1910 , step  1912 , step  1914 , step  1916 , step  1918 , and/or step  1920 , asset property data is provided and/or added to a smart discovery analyzer. In one or more embodiments, timestamp data is stored and/or correlated with respective asset property data. 
       FIG.  20    illustrates an exemplary flow diagram  2000  related to the discovery process  1200  according to one or more embodiments of the disclosure. In an embodiment a discovery process is performed for each asset in an asset array at step  2002 . Furthermore, for each asset in an asset array, an API call is executed at  2004 . In one or more embodiments, an HTML report is generated for each asset in response to the API call. In one or more embodiments, the HTML report includes asset information for the respective asset.  FIG.  21    illustrates an exemplary flow diagram  2100  related to generating an HTML report. The flow diagram  2100  includes a step  2102  for building an HTML frame and/or a cascading style sheets (CSS) frame. The flow diagram  2100  also includes a step  2104  for building an HTML table frame. For each asset in an asset array, the flow diagram  2100  includes a step  2106  for converting asset data into table rows of the HTML table frame and/or a step  2108  for appending the tables rows with the asset data to the HTML table frame. The flow diagram  2100  also includes a step  2110  for appending the HTML table frame to the HTML frame and/or the CSS frame. Furthermore, in one or more embodiments, the flow diagram  2100  includes a step  2112  for storing the HTML, frame and/or the CSS frame that includes the HTML, table frame associated with the asset data. 
       FIG.  22    illustrates an exemplary electronic interface  2200  according to one or more embodiments of the disclosure. In one or more embodiments, the electronic interface  2200  illustrates an offline discovery mode for determining asset property data for one or more assets via one or more network capture files stored in a database (e.g., the asset property database  318 ) and/or a memory (e.g., the memory  312 ). For example, in certain embodiments, a visualization of an offline source  2202  is provided via the electronic interface  2200  to facilitate determining and/or obtaining asset property data via the offline source  2202 . In one or more embodiments, a network capture file for the offline source  2202  can be saved by a network capture tool. In one or more embodiments, the offline source  2202  provides for saving captured asset property data to the offline source  2202  and/or reading one or more data files containing asset property data. 
       FIG.  23    illustrates an exemplary flow diagram  2300  according to one or more embodiments of the disclosure. At step  2302 , an asset discovery process is started. At step  2304 , it is determined whether to perform offline discovery. Offline discovery includes, for example, analysis of one or more network capture files rather than real-time analysis of one or more assets. In one or more embodiments, the one or more network capture files provide an identity, properties, and/or parameters for one or more assets within an industrial plant. In one or more embodiments, the one or more network capture files store asset property data for the one or more assets. If no, asset discovery is executed (e.g., with respect to one or more assets within the network  110 ) at step  2306  to return asset information  2308  (e.g., asset property data). For example, in one or more embodiments, execution of the asset discovery includes initiating a smart asset discovery engine (e.g., the asset discovery component  304 ) to discover, collect and/or analyze data associated with one or more assets within the network  110 . However, if no and at step  2310 , it is determined whether to upload a capture file (e.g., a network capture file). If yes, asset discovery is executed (e.g., with respect to one or more network capture files) at step  2312  to return asset information  2308  (e.g., asset property data). If no, a capture file is uploaded again at step  2314 . 
       FIG.  24    illustrates an exemplary system  2400  for discovery of a virtual machine according to one or more embodiments of the disclosure. The system  2400  includes an application  2402  and a server  2404 . In an embodiment, the application  2402  corresponds to the asset vulnerability assessment computer system  302  and the server  2404  corresponds to an asset. In an embodiment, the application  2402  transmits an API query  2406  to the server  2404 . In response to the API query  2406 , the server  2404  can transmit a response  2408  to the application  2402 . In one or more embodiments, at least a portion of asset property data for the server  2404  is determined based on the response  2408 . 
       FIG.  25    illustrates an exemplary flow diagram  2500  according to one or more embodiments of the disclosure. In one or more embodiments, the flow diagram  2500  is related to discovery of a virtual machine associated with the system  2400 . For example, in one or more embodiments, the flow diagram  2500  collets asset details for one or more virtual machines in a virtual machine infrastructure. The flow diagram  2500  includes a step  2502  for providing an IP address for a server and/or credentials to access the server. At step  2504 , it is determined whether any parameters are missing for the virtual machine. If yes, an error message is displayed (e.g., via the computing device  402 ) at step  2506 . If no, details for the virtual machine are checked at step  2508 . At step  2510 , it is determined whether the details for the virtual machine are successfully obtained. If no, an error message is displayed (e.g., via the computing device  402 ) at step  2512 . If yes, it is determined at step  2514  whether the virtual machine is currently in operation (e.g., powered on). If no, a message indicating that the virtual machine is powered-off is displayed (e.g., via the computing device  402 ) at step  2516 . If yes, an IP address, credentials, a hostname, and/or a status of the virtual machine if fetched at step  2518 . At step  2520 , it is determined whether the details for the virtual machine are successfully obtained. If no, an error message is displayed (e.g., via the computing device  402 ) at step  2522 . If yes, the details for the virtual machine are displayed at step  2524 . The details for the virtual machine include, for example, a power state of the virtual machine, a hostname for the virtual machine, an IP address for the virtual machine, a status of the virtual machine, version information for the virtual machine, and/or other details regarding properties of the virtual machine. 
       FIG.  26    illustrates an exemplary flow diagram  2600  according to one or more embodiments of the disclosure. In one or more embodiments, the flow diagram  2600  is related to discovery of a virtual machine associated with the system  2400 . For example, in one or more embodiments, the flow diagram  2600  discovers one or more alarms related to one or more virtual machines in a virtual machine infrastructure. The flow diagram  2600  includes a step  2602  for providing an IP address for a server and/or credentials to access the server. At step  2604 , it is determined whether any parameters are missing for the virtual machine. If yes, an error message is displayed (e.g., via the computing device  402 ) at step  2606 . If no, a connection is established with a server at step  2608 . At step  2610 , it is determined whether the details for the virtual machine are successfully obtained. If no, an error message is displayed (e.g., via the computing device  402 ) at step  2612 . If yes, one or more alarms for a virtual machine are analyzed at step  2614 . At step  2616 , it is determined whether the details for the virtual machine are successfully obtained. If no, the flow diagram  2600  ends. If yes, the details for the alarms are displayed at step  2618 . The details for the alarm include, for example, a one or more alarm occurrence timestamps, an IP address for a virtual machine associated with an alarm, formatted text associated with an alarm, and/or one or more other details related to an alarm. 
       FIG.  27    illustrates an exemplary flow diagram  2700  according to one or more embodiments of the disclosure. In one or more embodiments, the flow diagram  2700  is related to collection of hardware information for an asset via discovery of a virtual machine associated with the system  2400 . The flow diagram  2700  includes a step  2702  for providing an IP address for a server and/or credentials to access the server. At step  2704 , it is determined whether any parameters are missing for the virtual machine. If yes, an error message is displayed (e.g., via the computing device  402 ) at step  2706 . If no, a count of clusters related to the virtual machine are determined at step  2708 . At step  2710 , it is determined whether the details for the virtual machine are successfully obtained. If no, an error message is displayed (e.g., via the computing device  402 ) at step  2712 . If yes, a number of clusters related to the virtual machine are determined at step  2714 . At step  2716 , it is determined whether a count greater than zero is successfully fetched. If no, error details are displayed (e.g., via the computing device  402 ) at step  2717 . If yes, distributed resource scheduler (DRS) details are fetched at step  2718 . The DRS details include, for example, whether load balancing and/or another workload management process is enabled, an automation level related to load balancing and/or another workload management process, a mode for load balancing and/or another workload management process, and/or other DRS details. At step  2720 , it is determined whether the DRS details are successfully obtained. If no, error details are displayed (e.g., via the computing device  402 ) at step  2722 . If yes, the DRS details are displayed at step  2724 . 
       FIG.  28    illustrates an exemplary flow diagram  2800  according to one or more embodiments of the disclosure. In one or more embodiments, the flow diagram  2800  is related to collection of hardware information for an asset via discovery of a virtual machine associated with the system  2400 . The flow diagram  2800  includes a step  2802  for providing an IP address for a server and/or credentials (e.g., a username and/or password) to access the server. At step  2804 , it is determined whether any parameters are missing for the virtual machine. If yes, an error message is displayed (e.g., via the computing device  402 ) at step  2806 . If no, hardware information for the virtual machine is determined at step  2810 . The hardware information includes, for example, CPU core information, CPU socket information, CPU current clock, CPU maximum clock, CPU name, CPU load percentage, CPU type, CPU utilization, name of data store, size of datastore, free space in datastore, used space in datastore, free space as a percentage of total size of datastore, used space as a percentage of total size of datastore, datastore file system, datastore availability, errors associated with a database, total physical memory, free physical memory, free physical memory percentage, cluster memory utilization, and/or other hardware information. At step  2812 , it is determined whether the hardware information for the virtual machine is successfully obtained. If no, an error message is displayed (e.g., via the computing device  402 ) at step  2814 . If yes, the hardware information is displayed (e.g., via the computing device  402 ) at step  2816 . 
       FIG.  29    illustrates an exemplary flow diagram  2900  according to one or more embodiments of the disclosure. In one or more embodiments, the flow diagram  2900  is related to collection of hardware information for an asset via discovery of a virtual machine associated with the system  2400 . The flow diagram  2900  includes a step  2902  for providing an IP address for a server and/or credentials (e.g., a username and/or password) to access the server. At step  2904 , it is determined whether any parameters are missing for the virtual machine. If yes, an error message is displayed (e.g., via the computing device  402 ) at step  2906 . If no, a host count for the virtual machine is determined at step  2910 . The host count is, for example, a number of hosts managed by the virtual machine. At step  2912 , it is determined whether the host count for the virtual machine is successfully obtained. If no, an error message is displayed (e.g., via the computing device  402 ) at step  2914 . If yes, the host count is displayed (e.g., via the computing device  402 ) at step  2916 . 
       FIG.  30    illustrates a method  3000  for generating aggregated asset properties for assets discovered in a network to perform cybersecurity vulnerability assessment of the assets using the aggregated asset properties, in accordance with one or more embodiments described herein. The method  3000  is associated with the asset vulnerability assessment computer system  302 , for example. For instance, in one or more embodiments, the method  3000  is executed at a device (e.g., the asset vulnerability assessment computer system  302 ) with one or more processors and a memory. In one or more embodiments, the method  3000  begins at block  3002  that receives (e.g., by the asset vulnerability component  306 ) a request to perform an asset vulnerability assessment of one or more assets within a network, the request comprising an asset descriptor describing the one or more assets. The request provides one or more technical improvements such as, but not limited to, facilitating interaction with a computing device and/or extended functionality for a computing device. In one or more embodiments, the receiving the request includes receiving the request to perform the asset vulnerability assessment in response to an asset discovery process associated with the network. 
     At block  3004 , it is determined whether the request is processed. If no, block  3004  is repeated to determine whether the request is processed. If yes, the method  3000  proceeds to block  3006 . In response to the request, block  3006  obtains, based on the asset descriptor (e.g., by the asset vulnerability component  306 ), aggregated asset property data associated with the one or more assets. The obtaining provides one or more technical improvements such as, but not limited to, extended functionality for a computing device. In one or more embodiments, the obtaining the aggregated asset property data includes obtaining the aggregated asset property data from a formatted data structure that stores the aggregated asset property data. 
     In response to the request, the method  3000  also includes a block  3008  that performs (e.g., by the asset vulnerability component  306 ) the asset vulnerability assessment based on the aggregated asset property data and asset vulnerability signature data stored in an asset vulnerability signature repository. The performing the asset vulnerability assessment provides one or more technical improvements such as, but not limited to, improving accuracy of the dashboard visualization. 
     In response to the request, the method  3000  also includes a block  3010  that performs (e.g., by the action component  308 ) one or more actions associated with the network in response to determining that the asset vulnerability assessment satisfies a defined criterion. The performing the one or more actions provides one or more technical improvements such as, but not limited to, what and/or how to present information via a computing device. 
     In one or more embodiments, the method  3000  additionally or alternatively includes generating the aggregated asset property data by monitoring network traffic broadcasted to the one or more assets. In one or more embodiments, the method  3000  additionally or alternatively includes generating the aggregated asset property data based on one or more communications broadcasted by the one or more assets. In one or more embodiments, the method  3000  additionally or alternatively includes generating the aggregated asset property data by scanning, based on one or more data acquisition protocols associated with the one or more assets, one or more ports of the one or more assets. 
     In one or more embodiments, the method  3000  additionally or alternatively includes generating respective asset risk scores for the one or more assets based on the asset vulnerability assessment. Additionally or alternatively, the performing the one or more actions can include performing the one or more actions associated with the network based on the respective asset risk scores. 
     In one or more embodiments, the method  3000  additionally or alternatively includes generating respective asset risk scores for the one or more assets based on compliance analysis of hardware components associated with the one or more assets with respect to a virtualized infrastructure of the network. In one or more embodiments, the method  3000  additionally or alternatively includes generating respective asset risk scores for the one or more assets based on compliance analysis of software components associated with the one or more assets with respect to a virtualized infrastructure of the network. 
     In one or more embodiments, the method  3000  additionally or alternatively includes generating classification data associated with the one or more assets based on the aggregated asset property data. Additionally or alternatively, the performing the asset vulnerability assessment can include performing the asset vulnerability assessment based on the classification data. 
     In one or more embodiments, the method  3000  additionally or alternatively includes performing the asset discovery process in response to an action initiated via an electronic interface of a computing device associated with a user. In one or more embodiments, the method  3000  additionally or alternatively includes performing the asset discovery process in response to an action initiated in response to a timer satisfying a defined timer threshold value. In one or more embodiments, the method  3000  additionally or alternatively includes performing the asset discovery process based on one or more performance requirements associated with the one or more assets. 
     In one or more embodiments, the method  3000  additionally or alternatively includes determining role classification data indicating one or more roles for the one or more assets based on a set of heuristic tests associated with the aggregated asset property data. 
     In one or more embodiments, the method  3000  additionally or alternatively includes selecting the asset vulnerability signature data from the asset vulnerability signature repository based on a network zone identifier associated with the network. 
     In one or more embodiments, the method  3000  additionally or alternatively includes presenting, based on the asset vulnerability assessment, a visualization via an electronic interface of a computing device. In one or more embodiments, the method  3000  additionally or alternatively includes reconfiguring an asset from the one or more assets based on the asset vulnerability assessment. 
       FIG.  31    depicts an example system  3100  that may execute techniques presented herein.  FIG.  31    is a simplified functional block diagram of a computer that may be configured to execute techniques described herein, according to exemplary embodiments of the present disclosure. Specifically, the computer (or “platform” as it may not be a single physical computer infrastructure) may include a data communication interface  3160  for packet data communication. The platform also may include a central processing unit (“CPU”)  3120 , in the form of one or more processors, for executing program instructions. The platform may include an internal communication bus  3110 , and the platform also may include a program storage and/or a data storage for various data files to be processed and/or communicated by the platform such as ROM  3130  and RAM  3140 , although the system  3100  may receive programming and data via network communications. The system  3100  also may include input and output ports  3150  to connect with input and output devices such as keyboards, mice, touchscreens, monitors, displays, etc. Of course, the various system functions may be implemented in a distributed fashion on a number of similar platforms, to distribute the processing load. Alternatively, the systems may be implemented by appropriate programming of one computer hardware platform. 
     The foregoing method descriptions and the process flow diagrams are provided merely as illustrative examples and are not intended to require or imply that the steps of the various embodiments must be performed in the order presented. As will be appreciated by one of skill in the art the order of steps in the foregoing embodiments can be performed in any order. Words such as “thereafter,” “then,” “next,” etc. are not intended to limit the order of the steps; these words are simply used to guide the reader through the description of the methods. Further, any reference to claim elements in the singular, for example, using the articles “a,” “an” or “the” is not to be construed as limiting the element to the singular. 
     It is to be appreciated that ‘one or more’ includes a function being performed by one element, a function being performed by more than one element, e.g., in a distributed fashion, several functions being performed by one element, several functions being performed by several elements, or any combination of the above. 
     Moreover, it will also be understood that, although the terms first, second, etc. are, in some instances, used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first contact could be termed a second contact, and, similarly, a second contact could be termed a first contact, without departing from the scope of the various described embodiments. The first contact and the second contact are both contacts, but they are not the same contact. 
     The terminology used in the description of the various described embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various described embodiments and the appended claims, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     As used herein, the term “if” is, optionally, construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” is, optionally, construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context. 
     The systems, apparatuses, devices, and methods disclosed herein are described in detail by way of examples and with reference to the figures. The examples discussed herein are examples only and are provided to assist in the explanation of the apparatuses, devices, systems, and methods described herein. None of the features or components shown in the drawings or discussed below should be taken as mandatory for any specific implementation of any of these the apparatuses, devices, systems or methods unless specifically designated as mandatory. For ease of reading and clarity, certain components, modules, or methods may be described solely in connection with a specific figure. In this disclosure, any identification of specific techniques, arrangements, etc. are either related to a specific example presented or are merely a general description of such a technique, arrangement, etc. Identifications of specific details or examples are not intended to be, and should not be, construed as mandatory or limiting unless specifically designated as such. Any failure to specifically describe a combination or sub-combination of components should not be understood as an indication that any combination or sub-combination is not possible. It will be appreciated that modifications to disclosed and described examples, arrangements, configurations, components, elements, apparatuses, devices, systems, methods, etc. can be made and may be desired for a specific application. Also, for any methods described, regardless of whether the method is described in conjunction with a flow diagram, it should be understood that unless otherwise specified or required by context, any explicit or implicit ordering of steps performed in the execution of a method does not imply that those steps must be performed in the order presented but instead may be performed in a different order or in parallel. 
     Throughout this disclosure, references to components or modules generally refer to items that logically can be grouped together to perform a function or group of related functions. Like reference numerals are generally intended to refer to the same or similar components. Components and modules can be implemented in software, hardware, or a combination of software and hardware. The term “software” is used expansively to include not only executable code, for example machine-executable or machine-interpretable instructions, but also data structures, data stores and computing instructions stored in any suitable electronic format, including firmware, and embedded software. The terms “information” and “data” are used expansively and includes a wide variety of electronic information, including executable code; content such as text, video data, and audio data, among others; and various codes or flags. The terms “information,” “data,” and “content” are sometimes used interchangeably when permitted by context. 
     The hardware used to implement the various illustrative logics, logical blocks, modules, and circuits described in connection with the aspects disclosed herein can include a general purpose processor, a digital signal processor (DSP), a special-purpose processor such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA), a programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor can be a microprocessor, but, in the alternative, the processor can be any processor, controller, microcontroller, or state machine. A processor can also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Alternatively, or in addition, some steps or methods can be performed by circuitry that is specific to a given function. 
     In one or more example embodiments, the functions described herein can be implemented by special-purpose hardware or a combination of hardware programmed by firmware or other software. In implementations relying on firmware or other software, the functions can be performed as a result of execution of one or more instructions stored on one or more non-transitory computer-readable media and/or one or more non-transitory processor-readable media. These instructions can be embodied by one or more processor-executable software modules that reside on the one or more non-transitory computer-readable or processor-readable storage media. Non-transitory computer-readable or processor-readable storage media can in this regard comprise any storage media that can be accessed by a computer or a processor. By way of example but not limitation, such non-transitory computer-readable or processor-readable media can include random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), FLASH memory, disk storage, magnetic storage devices, or the like. Disk storage, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Blu-ray Disc™, or other storage devices that store data magnetically or optically with lasers. Combinations of the above types of media are also included within the scope of the terms non-transitory computer-readable and processor-readable media. Additionally, any combination of instructions stored on the one or more non-transitory processor-readable or computer-readable media can be referred to herein as a computer program product. 
     Many modifications and other embodiments of the inventions set forth herein will come to mind to one skilled in the art to which these inventions pertain having the benefit of teachings presented in the foregoing descriptions and the associated drawings. Although the figures only show certain components of the apparatus and systems described herein, it is understood that various other components can be used in conjunction with the supply management system. Therefore, it is to be understood that the inventions are not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Moreover, the steps in the method described above can not necessarily occur in the order depicted in the accompanying diagrams, and in some cases one or more of the steps depicted can occur substantially simultaneously, or additional steps can be involved. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation. 
     It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.