Patent Description:
Resource consumption touches every aspect of life. Resources are consumed for a wide variety of purposes every day. In some cases, energy is consumed in order to provide power to various components or to enable various devices or systems to function. In one example, energy in the form of electricity is consumed to enable the operations of computing devices or computing systems, appliances, air-conditioners, and many other components, entities, devices, systems, or services. In another example, energy in the form of natural gas is consumed to enable gas space heaters, gas water heaters, gas stoves, and other components, entities, devices, systems, or services to function.

Due to significant amounts of energy being consumed every day, it can be beneficial to provide tools or services for evaluating energy usage and ensuring that energy is being provided appropriately and continuously without interruption. In some instances, one or more components of an energy delivery network can be vulnerable or open to attack by various cyber threats, such as virus, malware, and hackers. Conventional approaches to evaluating and providing security for energy delivery can often times be insufficient, ineffective, or otherwise lacking. Moreover, in many cases, conventional approaches to energy observation, tracking, and protection do not provide adequate information or other resources to efficiently resolve various cyber issues. Accordingly, such concerns associated with conventional approaches can create challenges for and worsen the overall experience associated with energy delivery and consumption.

<CIT> describes selecting an asset from a plurality of heterogeneous assets of a business enterprise, where the user can then input a plurality of simulated risk factors for the selected asset into the system which receives this input. The risk assessment system can then generate a non-determinative simulated risk score using the simulated risk factors, the simulated risk score being a simulated measure of risk associated with the selected asset if the selected asset were to be associated with the plurality of simulated risk factors.

<CIT> describes a method for assessing a risk of one or more assets within an operational technology infrastructure by providing a database containing data relating to the one or more assets, calculating a threat score, vulnerability score, and an impact score for the one or more assets and determining the risk of the one or more assets based on the threat score, the vulnerability score and the impact score.

Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media that are configured to acquire a first set of data from a first group of data sources including a plurality of network components within an energy delivery network. A first metric indicating a likelihood that a particular network component, from the plurality of network components, is affected by one or more cyber vulnerabilities can be generated based on the first set of data. A second set of data can be acquired from a second group of data sources including a collection of services associated with the energy delivery network. A second metric indicating a calculated impact to at least a portion of the energy delivery network when the one or more cyber vulnerabilities affect the particular network component can be generated based on the second set of data. A third metric indicating an overall level of cybersecurity risk associated with the particular network component can be generated based on the first metric and the second metric.

In an embodiment, a plurality of third metrics including the third metric indicating the overall level of cybersecurity risk associated with the particular network component can be generated. Each third metric in the plurality of third metrics can indicate a respective overall level of cybersecurity risk associated with a respective network component in the plurality of network components. The plurality of network components can be ranked based on the plurality of third metrics to produce a ranked list of network components. At least a portion of the ranked list of network components can be provided to an energy provider that utilizes the energy delivery network.

In an embodiment, a set of visualizations for a set of network components identified in the ranked list of network components can be generated. Each visualization in the set of visualizations can represent a corresponding network component in the set of network components. Each visualization can be presented in association with a particular color determined based on at least one of a ranking for the corresponding network component or a corresponding overall level of cybersecurity risk associated with the corresponding network component.

In an embodiment, generating the third metric can further comprise applying a first weight value to the first metric to produce a first weighted metric. A second weight value can be applied to the second metric to produce a second weighted metric. The first weighted metric and the second weighted metric can be combined to produce the third metric.

In an embodiment, the first set of data can be acquired using at least a portion of a network cybersecurity service. The second set of data can be acquired using at least a portion of an energy management platform.

In an embodiment, the first set of data can be associated with detected network traffic within the energy delivery network. Generating the first metric can include analyzing the detected network traffic.

In an embodiment, analyzing the detected network traffic can include utilizing at least one of a syntax indicator, a computed indicator, or an advanced behavioral indicator. The likelihood that the particular network component is affected by the one or more cyber vulnerabilities can be calculated based on the at least one of the syntax indicator, the computed indicator, or the advanced behavioral indicator.

In an embodiment, the syntax indicator can be based on analysis of at least one of an Internet Protocol (IP) address associated with the detected network traffic or an email address associated with the detected network traffic.

In an embodiment, the computed indicator can be based on analysis of at least one of a message-digest algorithm hash value associated with the detected network traffic or a regular expression associated with the detected network traffic.

In an embodiment, the advanced behavioral indicator can be based on analysis of at least one of a multiple-step series of activities associated with the detected network traffic or a combination of multiple indicators associated with the detected network traffic.

In an embodiment, the second set of data can be associated with at least one of customer data relating to the energy delivery network, operations data relating to the energy delivery network, or economic data relating to the energy delivery network. Generating the second metric can include analyzing the at least one of the customer data, the operations data, or the economic data.

In an embodiment, the customer data can be associated with at least one of a customer count, an issue resolution time, a reliability index, or a customer criticality metric. The operations data can be associated with at least one of a labor cost, a materials cost, a physical damage likelihood metric, or a degree of redundancy. The economic data can be associated with at least one of an energy delivery cost, an equipment cost, or a regulatory penalty.

In an embodiment, at least some network components in the plurality of network components can be associated with operational technology. At least some services in the collection of services can be associated with information technology.

In an embodiment, the plurality of network components can include at least one of a router, a switch, a server, a firewall, a transformer, an energy distribution component, an energy transmission component, an energy generation component, or an energy delivery substation.

In an embodiment, the first group of data sources can further include at least one of a supervisory control and data acquisition (SCADA) command and control service, an enterprise firewall service, a log service, an intrusion prevention service, a security information and event management service (SIEM), or an intrusion protection service.

In an embodiment, the collection of services can include at least one of a phone service, a meter data management service, a customer information service, a geographic information service, a work management service, an enterprise asset management service, a smart meter head end service, an energy management service, a demand management service, an outage management service, a customer care and billing service, an enterprise communications service, or a threat and vulnerability detection library service.

In an embodiment, the third metric can be generated based on utilizing one or more machine learning processes to determine how the first metric and the second metric are to be combined to produce the third metric.

In an embodiment, the energy delivery network can include at least one of an electricity delivery network, an oil delivery network, or a gas delivery network.

Many other features, applications, embodiments, and/or variations of the disclosed technology will be apparent from the accompanying drawings and from the following detailed description. Additional and/or alternative implementations of the structures, systems, non-transitory computer readable media, and methods described herein can be employed without departing from the principles of the disclosed technology.

The figures depict various embodiments of the present disclosure for purposes of illustration only, wherein the figures use like reference numerals to identify like elements. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated in the figures may be employed without departing from the principles of the disclosed technology described herein.

Resources, such as energy, are consumed or used every day for a wide variety of purposes. In one example, consumers can use energy in the form of natural gas to power various appliances at home and businesses can use natural gas to operate various machinery. In another example, consumers and businesses can use energy in the form of electricity to power various electronic appliances and other electrical components, devices, or systems.

Energy consumption is facilitated by energy providers who supply energy to meet demand. Energy providers, such as utility companies, can provide one or more forms of energy, such as natural gas, oil, gasoline, electricity, etc. In some cases, energy providers can utilize energy delivery networks or systems to provide energy to their intended customers (i.e., users). In exchange, the energy providers can bill their customers for the energy consumed. Customers have to pay their energy bills if they wish to continue using the provided energy.

An energy delivery network (or system, service, etc.) can often times include an operational technology (OT) portion and an information technology (IT) portion. In general, operational technologies perform various tasks and activities to enable energy to be physically delivered to the customers. For example, the operational technology portion can correspond to an electric grid, including hardware and software components, configured to facilitate physical delivery and/or transmission of electricity to customers. Moreover, the information technology portion of the energy delivery network can sometimes be referred to as the enterprise portion of the energy delivery network. Information technologies can provide one or more services or systems enabling the energy provider to manage energy delivery, provide customer service, communicate with customers, and/or perform other tasks. For instance, information technologies can include a billing service or system that records which customers are to be billed and for how much money.

In some cases, one or more services and/or systems of information technologies can communicate with one or more components and/or systems of operational technologies. Furthermore, information technologies can connect to public networks, such as the internet. As such, in some instances, information technologies and operational technologies can be vulnerable to viruses, malware, hackers, errors, inadvertent/mistaken operation, and/or other cyber threats. However, conventional approaches are generally lacking in providing evaluation and protection measures for information technologies and operational technologies. Moreover, under conventional approaches, energy providers often face difficulty in determining how to resolve cybersecurity issues. Due to these and other reasons, conventional approaches can be insufficient, problematic, and inefficient. Accordingly, an improved approach to providing cybersecurity measures for energy delivery networks can be advantageous.

Various embodiments of the present disclosure can provide cybersecurity analysis based on (i.e., based at least in part on) operational technologies and information technologies. Systems, methods, and non-transitory computer readable media of the disclosed technology can be configured to acquire a first set of data from a first group of data sources including a plurality of network components within an energy delivery network. A first metric indicating a likelihood that a particular network component, from the plurality of network components, is affected (i.e., is currently affected, has been affected, may be affected, and/or will be affected, etc.) by one or more cyber vulnerabilities can be generated based on the first set of data. A second set of data can be acquired from a second group of data sources including a collection of services associated with the energy delivery network. A second metric indicating a calculated impact to at least a portion of the energy delivery network when the one or more cyber vulnerabilities affect the particular network component can be generated based on the second set of data. A third metric indicating an overall level of cybersecurity risk associated with the particular network component can be generated based on the first metric and the second metric. It is contemplated that there can be many variations and/or other possibilities. For instance, there can be many variations for generating the third metric based on one or more combinations or calculations utilizing the first metric and the second metric.

<FIG> illustrates an example scenario <NUM> in which cybersecurity analysis can be provided for operational technologies and information technologies, in accordance with an embodiment of the present disclosure. It should be understood that all examples herein are provided for illustrative purposes and that many variations are possible. In the example scenario <NUM>, an example cybersecurity analysis module <NUM> can be configured to acquire data from operational technologies and information technologies in an energy delivery network or system. Based on the acquired data, the cybersecurity analysis module <NUM> can facilitate providing cybersecurity analysis based on operational technologies and information technologies in the energy delivery network.

As shown in the example of <FIG>, the energy delivery network can include an information technology portion (IT) and an operational technology (OT) portion. In this example, the information technology portion can be represented as the left side of the dotted vertical line <NUM>, while the operational technology portion can be represented as the right side of the dotted vertical line <NUM>. As discussed previously, operational technologies of the energy delivery network can enable energy to be monitored, controlled, and/or physically delivered or provided to intended customers. Information technologies can provide various services and functions other than the physical delivery or transmission of the energy to the customers.

In the example scenario <NUM>, operational technologies of the energy delivery network can include one or more generators <NUM>, transmission systems <NUM>, and distribution systems <NUM>. Further, there can be a plurality of energy delivery substations, such as Substation A <NUM> and Substation B <NUM>. Each respective substation can deliver energy to a respective group of customers. For instance, the example scenario <NUM> shows that Substation A <NUM> can provide energy to various customers, such as Building A <NUM>, Building B <NUM>, and Building C <NUM>. Furthermore, each substation can include a plurality of components (or systems). Such components can include, but are not limited to, one or more communications components <NUM>, firewalls <NUM>, routers <NUM>, network switches <NUM>, servers <NUM>, and transformers <NUM>, breakers <NUM>, electrical switches <NUM>, and reclosers <NUM>. In some cases, components such as the firewalls <NUM>, routers <NUM>, network switches <NUM>, servers <NUM>, etc. can be associated with the information technology portion while components such as the transformers <NUM>, breakers <NUM>, electrical switches <NUM>, reclosers <NUM>, etc. can be associated with the operational technology portion. These components can be configured to facilitate delivering energy to the customers. For instance, the one or more routers <NUM> can direct information to facilitate energy delivery. Switches such as the one or more electrical switches <NUM> can toggle to cause energy to be transmitted to its intended destination. The one or more transformers <NUM> can facilitate energy transfer between circuits, such as via induction. The one or more communications components <NUM>, one or more servers <NUM>, and/or control components can instruct the routers <NUM>, switches <NUM>, and/or transformers <NUM> to operate appropriately. The one or more firewalls <NUM> can attempt to prevent undesirable or inappropriate traffic. It should be understood that many variations are possible.

In addition, information technologies of the energy delivery network can include a collection of services or systems. Examples of the services (or systems) can include, but are not limited to, a phone system <NUM>, a meter data management (MDM) system <NUM>, a billing system <NUM>, a customer service system <NUM>, an outage management system <NUM>, and a database(s) <NUM>. Further, each of the information technology systems or services can be connected to a public network, such as the internet <NUM>. Again, there can be many variations or other possibilities.

In some instances, one or more information technology services (or systems) of the energy delivery network can connect with one or more operational technology components (or systems) of the energy delivery network. In the example scenario <NUM>, the meter data management system <NUM> and the billing system <NUM> of the information technology portion can be connected to Substation A <NUM> of the operation technology portion. Although not shown in this example, other information technologies can be connected to various operational technologies as well. Accordingly, operation technologies can also connect to public networks, such as by connecting to the internet <NUM> via information technologies. As a result, in some cases, viruses, malware, hackers, errors (e.g., typos, invalid data, etc.), or other cyber threats can negatively affect the information technologies as well as the operation technologies. In some cases, one or more firewalls <NUM> can be set up between information technology systems and operational technology systems. However, due to the quantity, variety, and ever-changing nature of cyber threats, such firewalls <NUM> are often times insufficient or inadequate to protect against potential cyber threats to information technologies and operational technologies.

As discussed above, the cybersecurity analysis module <NUM> can be configured to facilitate providing cybersecurity analysis based on (i.e., based at least in part on) operational technologies and information technologies in the energy delivery network. As shown in the example scenario <NUM>, the cybersecurity analysis module <NUM> can request, fetch, retrieve, monitor, or otherwise acquire data from various components, services, and/or systems of the operational and information technology portions of the energy delivery network. In some implementations, the data can be acquired in (or near) real-time and/or can be acquired at various times (e.g., every day, every hour, every minute, every second, hundreds of times per second, thousands of times per second, etc.). In one example, the cybersecurity analysis module <NUM> can acquire or monitor firewall data which indicates whether the firewalls are experiencing unusual, abnormal, or unexpected network traffic. In another example, the cybersecurity analysis module <NUM> can acquire or monitor server data which indicates whether the servers are being used in an unusual, abnormal, or unexpected manner. In another example, the cybersecurity analysis module <NUM> can acquire or monitor transformer data which indicates whether the transformers are in an unusual, abnormal, or unexpected state. Many variations are possible.

The cybersecurity analysis module <NUM> can process the acquired data and provide a detailed analysis of various cybersecurity issues for the operational technologies and the information technologies. In some embodiments, the cybersecurity analysis module <NUM> can provide or be utilized with a control panel or dashboard that presents cybersecurity information (e.g., the detailed analysis) to an entity, such as a cybersecurity analyst or manager (or administrator) who is responsible for determining how to proceed when a multitude of cybersecurity risks are detected within the energy delivery network. The cybersecurity information or analysis can, for example, specify where the cybersecurity risks are located (e.g., where they are currently located, where they may be located in the future, etc.), who the affected customers are, where the affected customers are, and/or a list of specific items to be examined in attempt to mitigate the cybersecurity risks, etc. More details regarding the cybersecurity analysis module <NUM> will be provided below with reference to <FIG>.

<FIG> illustrates an example system <NUM> including an example cybersecurity analysis module <NUM> configured to facilitate providing cybersecurity analysis based on operational technologies and information technologies, in accordance with an embodiment of the present disclosure. In some embodiments, the cybersecurity analysis module <NUM> of <FIG> can be implemented as the example cybersecurity analysis module <NUM>. As shown in <FIG>, the example cybersecurity analysis module <NUM> can include a cyber vulnerability module <NUM>, a potential impact module <NUM>, and a cybersecurity risk module <NUM>. In some instances, the example system <NUM> can also include at least a first group of data sources <NUM> and a second group of data sources <NUM>. The components (e.g., modules, elements, data sources, etc.) shown in this figure and all figures herein are exemplary only, and other implementations may include additional, fewer, integrated, or different components. Some components may not be shown so as not to obscure relevant details.

In some embodiments, the cybersecurity analysis module <NUM> can be implemented, in part or in whole, using software, hardware, or any combination thereof. In general, a module can be associated with software, hardware, or any combination thereof. In some implementations, one or more functions, tasks, and/or operations of modules can be carried out or performed by software routines, software processes, hardware components, and/or any combination thereof. In some cases, the cybersecurity analysis module <NUM> can be implemented as software running on one or more computing devices or systems. In one example, at least a portion of the cybersecurity analysis module <NUM> can be implemented via one or more computing systems in a networked environment, such as via one or more remote or cloud servers. In another example, at least a portion of the cybersecurity analysis module <NUM> can be implemented within an application (e.g., app) on a computing device or system such as a smartphone, tablet, laptop, or desktop computer. In some embodiments, the cybersecurity analysis module <NUM> can be implemented by or with an energy management platform, such as the energy management platform <NUM> of <FIG> or the energy management platform <NUM> of <FIG>. The energy management platform may provide the functionality(ies) of the cybersecurity analysis module <NUM> as a service or through software. The cybersecurity analysis module <NUM> can, in some instances, be implemented within a proprietary program used by an energy provider, such as a utility company. In some cases, the cybersecurity analysis module <NUM> can be implemented with a network resource, such as a website or webpage. It is contemplated that many variations are possible.

As discussed, the cybersecurity analysis module <NUM> can be configured to acquire data from various components, services, systems, etc., of the operational technology portion and the information technology portion of the energy delivery network. In some embodiments, the cybersecurity analysis module <NUM> can utilize the cyber vulnerability module <NUM> to facilitate acquiring a first set of data from the first group of data sources <NUM>. The first group of data sources <NUM> can include, but is not limited to, a plurality of network components (i.e., energy network components) within the energy delivery network, such as various operational technology components or systems. Examples of the network components can include, but are not limited to, at least one of a router, a switch, a server, a firewall, a transformer, an energy distribution component, an energy transmission component, an energy generation component, and/or an energy delivery substation, etc. Additionally, the cyber vulnerability module <NUM> can also be configured to facilitate generating, based on (i.e., based at least in part on) the first set of data, a first metric indicating a likelihood that a particular network component, from the plurality of network components, is affected (e.g., has been affected, is currently affected, and/or may be affected in the future, etc.) by one or more cyber vulnerabilities. The cyber vulnerability module <NUM> will be discussed in more detail below with reference to <FIG>.

Moreover, the cybersecurity analysis module <NUM> can utilize the potential impact module <NUM> to facilitate acquiring a second set of data from the second group of data sources <NUM>. The second group of data sources <NUM> can include, but is not limited to, a collection of services associated with the energy delivery network, such as various information technology services or systems. The potential impact module <NUM> can also be configured to facilitate generating, based on the second set of data, a second metric indicating a calculated impact to at least a portion of the energy delivery network when the one or more cyber vulnerabilities affect the particular network component. More details regarding the potential impact module <NUM> will be provided below with reference to <FIG>.

In some embodiments, at least some network components in the plurality of network components can be associated with operational technology. In some embodiments, at least some services in the collection of services can be associated with information technology. However, it so also contemplated that at least some of the network components can be associated with information technology and that at least some of the collection of services can be associated with operational technology.

Furthermore, the cybersecurity analysis module <NUM> can utilize the cybersecurity risk module <NUM> to facilitate generating, based on the first metric and the second metric, a third metric indicating an overall level of cybersecurity risk associated with the particular network component. The third metric can, for instance, represent a measure of cyber threat severity, for the particular network component, that takes into consideration the likelihood that the particular network component has one or more cyber vulnerabilities as well as the calculated potential impact to the energy delivery network (or at least a particular portion thereof) if and when the particular network component is affected by the one or more cyber vulnerabilities.

In some implementations, the cybersecurity risk module <NUM> can generate the third metric from a defined combination of the first metric and the second metric. In one example, in order to generate the third metric, the cybersecurity risk module <NUM> can apply a first weight value to the first metric to produce a first weighted metric. The cybersecurity risk module <NUM> can further apply a second weight value to the second metric to produce a second weighted metric. The cybersecurity risk module <NUM> can then combine the first weighted metric and the second weighted metric to produce the third metric. In another example, the energy provider (e.g., the utility company) can define how the first and second metrics are to be combined to produce the third metric. In a further example, the third metric can be generated based on utilizing one or more machine learning processes to determine how the first metric and the second metric are to be combined to produce the third metric. The one or more machine learning processes can ensure that the cybersecurity analysis module <NUM> is dynamically updated and is configured to detect emerging cybersecurity threats and vulnerabilities. In some cases, the machine learning processes can incorporate utility user feedback regarding the authenticity of detected threats and vulnerabilities as well as end customer impacts due to such threats and vulnerabilities. The machine learning processes can take into consideration data and user input regarding the authenticity and/or impact of detected threats and vulnerabilities by updating approaches to cybersecurity risk determination, traffic detection/monitoring, and/or impact calculation. It is contemplated that there can be numerous variations and/or other possibilities.

Furthermore, in some implementations, the cybersecurity risk module <NUM> can be configured to generate a plurality of third metrics, including the third metric indicating the overall level of cybersecurity risk associated with the particular network component, as discussed previously. Each third metric in the plurality of third metrics can indicate a respective overall level of cybersecurity risk associated with a respective network component in the plurality of network components. The cybersecurity risk module <NUM> can further rank the plurality of network components based on the plurality of third metrics to produce a ranked list of network components. Additionally, the cybersecurity risk module <NUM> can provide at least a portion of the ranked list of network components (e.g., at least a specified number of highest ranked network components) to the energy provider that utilizes the energy delivery network. Accordingly, the ranked list (and/or the plurality of third metrics) can help the energy provider determine priorities for examining the network components, repairing the network components, recording actions taken on the network components, recording the state of cybersecurity policy compliance of the network components, or otherwise addressing cybersecurity concerns at the network components. In some cases, the ranked list (and/or the plurality of third metrics) can be provided in association with a large amount of information, such as information that indicates which network components have been attacked, are currently being attacked, and/or will be attacked by cyber threats, which customers are affected, and so forth. Many variations are possible.

<FIG> illustrates an example cyber vulnerability module <NUM> configured to facilitate providing cybersecurity analysis based on operational technologies and information technologies, in accordance with an embodiment of the present disclosure. In some embodiments, the cyber vulnerability module <NUM> of <FIG> can be implemented as the example cyber vulnerability module <NUM>. As shown in <FIG>, the cyber vulnerability module <NUM> can include a vulnerability data processing module <NUM> and a vulnerability metric module <NUM>.

As discussed above, the cyber vulnerability module <NUM> can facilitate acquiring a first set of data from a first group of data sources. In some embodiments, the cyber vulnerability module <NUM> can utilize the vulnerability data processing module <NUM> to acquire the first set of data from the first group of data sources. In some embodiments, the first group of data sources can include, but is not limited to, at least one of a supervisory control and data acquisition (SCADA) command and control service, an enterprise firewall service, a log service, an intrusion prevention service, a security information and event management service (SIEM), and/or an intrusion protection service, etc..

Moreover, the first set of data can, for instance, be referred to as vulnerability data or cyber vulnerability data. Based on the cyber vulnerability data, the vulnerability data processing module <NUM> can determine one or more potential cyber vulnerabilities (if any) associated with network components in the energy delivery network, as well as various properties or metadata associated with the potential cyber vulnerabilities.

In some instances, the first set of data can be acquired using at least a portion of a network cybersecurity service, such as an end point protection provider, a security information event monitoring (SIEM) provider, an intrusion prevention provider, a behavioral threat detection provider, and/or an operational technology security product provider, etc. In some cases, the network cybersecurity service can correspond to a third-party service.

Furthermore, as discussed previously, the cyber vulnerability module <NUM> can be configured to generate, based on the first set of data, a first metric (i.e., a cyber vulnerability metric) indicating a likelihood that a particular network component is affected by one or more cyber vulnerabilities, such as viruses, malware, hackers, errors, etc. The cyber vulnerability module <NUM> can utilize the vulnerability metric module <NUM> to generate the first metric. In some cases, the first set of data can be associated with detected network traffic within the energy delivery network, such as network traffic detected at various network components within the energy delivery network. The vulnerability metric module <NUM> can generate the first metric based on analyzing the detected network traffic.

In some embodiments, analyzing the detected network traffic can include utilizing at least one of a syntax (or rule-based) indicator, a computed (or analytical) indicator, and/or an advanced behavioral indicator, etc. Moreover, the likelihood that the particular network component is affected by the one or more cyber vulnerabilities can be calculated, by the vulnerability metric module <NUM>, based on the at least one of the syntax indicator, the computed indicator, or the advanced behavioral indicator.

In some cases, the cyber vulnerability module <NUM> can identify patterns and develop rules or syntax indicators for detecting illegitimate activities particular to the energy delivery network. For example, if the cyber vulnerability module <NUM> detects that an admin login fails to sufficiently correlate with the admin's deduced physical presence and/or that an unexpected pair of Internet Protocol (IP) addresses has appeared, then the first metric can be increased. In some instances, the cyber vulnerability module <NUM> can perform analytics and/or detect computed indicators. For example, if the cyber vulnerability module <NUM> detects protocol anomalies, unexpected device appearances, unexpected MAC addresses, unauthorized access attempts, and/or unexpected privilege escalations (e.g., a user unexpectedly attempting to perform an unpermitted task), etc., then the first metric can be increased. In some instances, the cyber vulnerability module <NUM> can detect advanced behavior indicators. For example, if the cyber vulnerability module <NUM> detects unexpected bandwidth spikes, unexpected CPU usage spikes, a command received at an unexpected time, and/or a trust boundary violation, then the first metric can be increased. It should be appreciated that there can be many variations or other possibilities.

In one example, the syntax indicator can be based on analysis of at least one of an Internet Protocol (IP) address associated with the detected network traffic or an email address associated with the detected network traffic. In this example, if the IP address and/or the email address is determined to be linked to an illegitimate source, system, entity, account, etc., then the first metric can be increased. In another example, the computed indicator can be based on analysis of at least one of a message-digest algorithm (e.g., MD5) hash value associated with the detected network traffic or a regular expression (e.g., spam message keyword) associated with the detected network traffic. In this example, if the hash value is determined to be related to a virus, malware, Trojan, etc., and/or if the regular expression is determined to be related to a spam communication, a phishing message, a piece of junk mail, etc., then the first metric can be increased. In a further example, the advanced behavioral indicator can be based on analysis of at least one of a multiple-step series of activities associated with the detected network traffic or a combination of multiple indicators associated with the detected network traffic. In this example, if a particular sequence of multiple activities is unexpected/unusual and/or if a significant quantity of unexpected/unusual activity indicators are detected, then the first metric can be increased. Again, many variations are possible.

<FIG> illustrates an example potential impact module <NUM> configured to facilitate providing cybersecurity analysis based on operational technologies and information technologies, in accordance with an embodiment of the present disclosure. In some embodiments, the potential impact module <NUM> of <FIG> can be implemented as the example potential impact module <NUM>. In some instances, the potential impact module <NUM> can recognize or have access to information about how the energy delivery network is configured (e.g., which customers are connected to which substations, how customers are connected to substations, how much energy is passing through each substation, how customers use delivered energy, etc.). Such information can assist the potential impact module <NUM> to facilitate providing cybersecurity analysis. As shown in <FIG>, the potential impact module <NUM> can include an impact data processing module <NUM> and an impact metric module <NUM>.

The potential impact module <NUM> can utilize the impact data processing module <NUM> to facilitate acquiring a second set of data from a second group of data sources including a collection of services associated with the energy delivery network. The collection of services can, for instance, include information technology services or systems. Examples of the collection of services can include, but are not limited to, at least one of a phone service, a meter data management service, a customer information service, a geographic information service, a work management service, an enterprise asset management service, a smart meter head end service, an energy management service, a demand management service, an outage management service, a customer care and billing service, an enterprise communications service, and/or a threat and vulnerability detection library service, etc. In some instances, the impact data processing module <NUM> can acquire the second set of data using at least a portion of an energy management platform (e.g., the energy management platform <NUM> of <FIG>, the energy management platform <NUM> of <FIG>). For instance, at least the portion of the energy management platform can be implemented as the impact data processing module <NUM>, can perform one or more functions of the impact data processing module <NUM>, and/or can operate in conjunction with the impact data processing module <NUM> to acquire the second set of data from the second group of data sources.

Moreover, the potential impact module <NUM> can utilize the impact metric module <NUM> to facilitate generating, based on the second set of data, a second metric (i.e., a potential impact metric) indicating a calculated impact to at least a portion of the energy delivery network when one or more cyber vulnerabilities affect the particular network component. In some cases, the second set of data can be associated with at least one of customer data relating to the energy delivery network, operations data relating to the energy delivery network, or economic data relating to the energy delivery network.

In some embodiments, the impact metric module <NUM> can generate the second metric based on analyzing the at least one of the customer data, the operations data, or the economic data. In one example, the customer data can be associated with at least one of a customer count, an issue resolution time, a reliability index, and/or a customer criticality metric, etc. In this example, if the customer count associated with the particular network component is larger, if the amount of time to resolve the one or more cyber vulnerabilities is higher, if the particular network component has an impact reliability index or score that at least meets a specified impact reliability threshold, and/or if the criticality of the customers is higher (e.g., the customer is a hospital, police department, fire department, etc.), then the second metric can be increased. In another example, the operations data can be associated with at least one of a labor cost, a materials cost, a physical damage likelihood metric, and/or a degree of redundancy, etc. In this example, if the cost(s) and/or the damage likelihood metric is higher and/or if the degree of redundancy (e.g., back-up systems) is lower, then the second metric can be increased. In a further example, the economic data can be associated with at least one of an energy delivery cost, an equipment cost, and/or a regulatory penalty, etc. In this example, if the costs(s) and/or penalties are higher, then the second metric can be increased. Again, many variations are possible.

<FIG> illustrates an example block diagram <NUM> associated with providing cybersecurity analysis based on operational technologies and information technologies, in accordance with an embodiment of the present disclosure. The block diagram <NUM> shows an example of how cybersecurity analysis performed based on the disclosed technology can provide, calculate, determine, or otherwise generate a cybersecurity risk metric (or score) <NUM> for a particular component (or for a particular set of components) in an energy delivery network. The cybersecurity analysis can indicate whether particular components are experiencing unusual, abnormal, or unexpected activity. Based on the cybersecurity analysis, a priority or urgency level for repairing components affected by cyber threats can also be determined. Again, all examples provided herein are for illustrative purposes and it should be understood that numerous variations are possible.

As shown in the example block diagram <NUM>, the cybersecurity risk metric <NUM> (i.e., the third metric generated by the cybersecurity risk module <NUM> of <FIG>) can be based on (i.e., based at least in part on) combining the cyber vulnerability metric <NUM> and the potential impact metric <NUM>. The cyber vulnerability metric <NUM> can be generated based on traffic detection <NUM>. For example, generating the cyber vulnerability metric <NUM> can include analyzing detected network traffic, as discussed previously. Furthermore, the potential impact metric <NUM> can be generated based on customer impact <NUM>, operations impact <NUM>, and/or economic impact <NUM>. For instance, as discussed above, generating the potential impact metric <NUM> can include analyzing at least one of customer data relating to the energy delivery network, operations data relating to the energy delivery network, or economic data relating to the energy delivery network. It should be appreciated that there can be many variations or other possibilities.

<FIG> illustrates an example screenshot <NUM> associated with providing cybersecurity analysis based on operational technologies and information technologies, in accordance with an embodiment of the present disclosure. The example screenshot <NUM> shows an example interface for providing cybersecurity analysis based on operational technologies and information technologies.

In some case, the example interface can provide an interface portion <NUM> that presents information about activity(ies) associated with detected network traffic. In some embodiments, the example interface can provide another interface portion <NUM> that presents information about customers, such as in the form of a ranked list of customers who are affected or at risk of being affected by cyber threats. Moreover, in some instances, the example interface can provide an additional interface portion <NUM> that presents information about network components, equipment, and/or assets. The interface portion <NUM> can, for example, present a ranked list of network components that are affected or at risk of being affected by cyber threats. In some cases, interface portion <NUM> can also present information about why or how network components are affected or at risk of being affected by cyber threats.

In some implementations, a set of visualizations (e.g., graphical elements) for a set of network components identified in the ranked list of network components can be generated. The example interface can further provide an interface portion <NUM> that presents the generated set of visualizations for the set of network components identified in the ranked list of network components. Each visualization in the set of visualizations can represent a corresponding network component in the set of network components. In some instances, each visualization can be presented in association with a particular color determined based on at least one of a ranking for the corresponding network component or a corresponding overall level of cybersecurity risk associated with the corresponding network component. Again, the example screenshot <NUM> and other examples herein are provided for illustrative purposes and it is contemplated that many variations are possible.

<FIG> illustrates an example method <NUM> associated with providing cybersecurity analysis based on operational technologies and information technologies, in accordance with an embodiment of the present disclosure. It should be understood that there can be additional, fewer, or alternative steps performed in similar or alternative orders, or in parallel, within the scope of the various embodiments unless otherwise stated.

At block <NUM>, the example method <NUM> can acquire a first set of data from a first group of data sources including a plurality of network components within an energy delivery network. At block <NUM>, the example method <NUM> can generate, based on the first set of data, a first metric indicating a likelihood that a particular network component, from the plurality of network components, is affected (e.g., has been affected, is currently affected, and/or may be affected in the future, etc.) by one or more cyber vulnerabilities. At block <NUM>, the example method <NUM> can acquire a second set of data from a second group of data sources including a collection of services associated with the energy delivery network. At block <NUM>, the example method <NUM> can generate, based on the second set of data, a second metric indicating a calculated impact to at least a portion of the energy delivery network when the one or more cyber vulnerabilities affect the particular network component. At block <NUM>, the example method <NUM> can generate, based on the first metric and the second metric, a third metric indicating an overall level of cybersecurity risk associated with the particular network component.

In some cases, the overall level of cybersecurity risk as indicated by the third metric can correspond to a proprietary composite measure of cybersecurity risk. The proprietary composite measure of cybersecurity risk can, in some embodiments, be produced or outputted based on (i.e., based at least in part on) information about cyber vulnerability(ies) and information about impact. In some instances, a cyber vulnerability can refer to an intrinsic susceptibility of a component to one or more cyber threats and can include information about provided or inputted cybersecurity risk (e.g., a third-party-provided/calculated likelihood of exploitation of a particular vulnerability by an entity at a particular time on a particular component/system). The provided or inputted cybersecurity risk can sometimes incorporate financial or service impact. Accordingly, the proprietary composite measure of cybersecurity risk can be produced or outputted based on information about cyber vulnerability(ies) (which can include inputted/provided cybersecurity risk data) and information about impact. It should be appreciated that many variations are possible.

<FIG> illustrates an example method <NUM> associated with providing cybersecurity analysis based on operational technologies and information technologies, in accordance with an embodiment of the present disclosure. As discussed, it should be understood that there can be additional, fewer, or alternative steps performed in similar or alternative orders, or in parallel, within the scope of the various embodiments unless otherwise stated.

At block <NUM>, the example method <NUM> can generate a plurality of third metrics including the third metric indicating the overall level of cybersecurity risk associated with the particular network component. Each third metric in the plurality of third metrics can indicate a respective overall level of cybersecurity risk associated with a respective network component in the plurality of network components. At block <NUM>, the example method <NUM> can rank the plurality of network components based on the plurality of third metrics to produce a ranked list of network components. At block <NUM>, the example method <NUM> can provide at least a portion of the ranked list of network components to an energy provider that utilizes the energy delivery network.

<FIG> illustrates an example method <NUM> associated with providing cybersecurity analysis based on operational technologies and information technologies, in accordance with an embodiment of the present disclosure. Again, it should be appreciated that there can be additional, fewer, or alternative steps performed in similar or alternative orders, or in parallel, within the scope of the various embodiments unless otherwise stated.

At block <NUM>, the example method <NUM> can apply a first weight value to the first metric to produce a first weighted metric. At block <NUM>, the example method <NUM> can apply a second weight value to the second metric to produce a second weighted metric. At block <NUM>, the example method <NUM> can combine the first weighted metric and the second weighted metric to produce the third metric.

It is further contemplated that there can be many other uses, applications, and/or variations associated with the various embodiments of the present disclosure. For instance, in some cases, the example cybersecurity analysis module <NUM> of <FIG> can be implemented, in part or in whole, as software, hardware, or any combination thereof, as discussed above. In some embodiments, the cybersecurity analysis module <NUM> can be implemented with an energy management platform, such as the energy management platform <NUM> of <FIG> and/or the energy management platform <NUM> of <FIG>.

<FIG> illustrates an example environment <NUM> for energy management, in accordance with an embodiment of the present disclosure. The environment <NUM> includes an energy management platform <NUM>, external data sources <NUM>-n, an enterprise <NUM>, and a network <NUM>. The energy management platform <NUM> can provide functionality to allow the enterprise <NUM> to track, analyze, and optimize energy usage of the enterprise <NUM>. The energy management platform <NUM> may constitute an analytics platform. The analytics platform may handle data management, multi-layered analysis, and data visualization capabilities for all applications of the energy management platform <NUM>. The analytics platform may be specifically designed to process and analyze significant volumes of frequently updated data while maintaining high performance levels.

The energy management platform <NUM> may communicate with the enterprise <NUM> through user interfaces (Uls) presented by the energy management platform <NUM> for the enterprise <NUM>. The Uls may provide information to the enterprise <NUM> and receive information from the enterprise <NUM>. The energy management platform <NUM> may communicate with the external data sources <NUM>-n through APIs and other communication interfaces. Communications involving the energy management platform <NUM>, the external data sources <NUM>-n, and the enterprise <NUM> are discussed in more detail herein.

The energy management platform <NUM> may be implemented as a computer system, such as a server or series of servers and other hardware (e.g., applications servers, analytic computational servers, database servers, data integrator servers, network infrastructure (e.g., firewalls, routers, communication nodes)). The servers may be arranged as a server farm or cluster. Embodiments of the present disclosure may be implemented on the server side, on the client side, or a combination of both. For example, embodiments of the present disclosure may be implemented by one or more servers of the energy management platform <NUM>. As another example, embodiments of the present disclosure may be implemented by a combination of servers of the energy management platform <NUM> and a computer system of the enterprise <NUM>.

The external data sources <NUM>-n may represent a multitude of possible sources of data relevant to energy management analysis. The external data sources <NUM>-n may include, for example, grid and utility operational systems, meter data management (MDM) systems, customer information systems (CIS), billing systems, utility customer systems, utility enterprise systems, utility energy conservation measures, and rebate databases. The external data sources <NUM>-n also may include, for example, building characteristic systems, weather data sources, third-party property management systems, and industry-standard benchmark databases.

The enterprise <NUM> may represent a user (e.g., customer) of the energy management platform <NUM>. The enterprise <NUM> may include any private or public concern, such as large companies, small and medium businesses, households, individuals, governing bodies, government agencies, non-govemmental organizations, nonprofits, etc. The enterprise <NUM> may include energy providers and suppliers (e.g., utilities), energy service companies (ESCOs), and energy consumers. The enterprise <NUM> may be associated with one or many facilities distributed over many geographic locations. The enterprise <NUM> may be associated with any purpose, industry, or other type of profile.

The network <NUM> may use standard communications technologies and protocols. Thus, the network <NUM> may include links using technologies such as Ethemet, <NUM>, worldwide interoperability for microwave access (WiMAX), <NUM>, <NUM>, CDMA, GSM, LTE, digital subscriber line (DSL), etc. Similarly, the networking protocols used on the network <NUM> may include multiprotocol label switching (MPLS), transmission control protocol/Internet protocol (TCP/IP), User Datagram Protocol (UDP), hypertext transport protocol (HTTP), simple mail transfer protocol (SMTP), file transfer protocol (FTP), and the like. The data exchanged over the network <NUM> may be represented using technologies and/or formats including hypertext markup language (HTML) and extensible markup language (XML). In addition, all or some links may be encrypted using conventional encryption technologies such as secure sockets layer (SSL), transport layer security (TLS), and Internet Protocol security (IPsec).

In an embodiment, each of the energy management platform <NUM>, the external data sources <NUM>-n, and the enterprise <NUM> may be implemented as a computer system (or device). The computer system (or device) may include one or more machines, each of which may be implemented as machine <NUM> of <FIG>, which is described in further detail herein.

<FIG> illustrates an example energy management platform <NUM>, in accordance with an embodiment of the present disclosure. In some embodiments, the example energy management platform <NUM> can be implemented as the energy management platform <NUM> of <FIG>. In an embodiment, the energy management platform <NUM> may include a data management module <NUM>, applications servers <NUM>, relational databases <NUM>, and key/value stores <NUM>. In some embodiments, the energy management platform <NUM> can also include a cybersecurity analysis module (e.g., the cybersecurity analysis module <NUM> of <FIG>).

The data management module <NUM> may support the capability to automatically and dynamically scale a network of computing resources for the energy management platform <NUM> according to demand on the energy management platform <NUM>. The dynamic scaling supported by the data management module <NUM> may include the capability to provision additional computing resources (or nodes) to accommodate increasing computing demand. Likewise, the data management module <NUM> may include the capability to release computing resources to accommodate decreasing computing demand. The data management module <NUM> may include one or more action(s) <NUM>, a queue <NUM>, a dispatcher <NUM>, a resource manager <NUM>, and a cluster manager <NUM>.

The actions <NUM> may represent the tasks that are to be performed in response to requests that are provided to the energy management platform <NUM>. Each of the actions <NUM> may represent a unit of work to be performed by the applications servers <NUM>. The actions <NUM> may be associated with data types and bound to engines (or modules). The requests may relate to any task supported by the energy management platform <NUM>. For example, the request may relate to, for example, analytic processing, loading energy-related data, retrieving an energy star reading, retrieving benchmark data, etc. The actions <NUM> are provided to the action queue <NUM>.

The action queue <NUM> may receive each of the actions <NUM>. The action queue <NUM> may be a distributed task queue and represents work that is to be routed to an appropriate computing resource and then performed.

The dispatcher <NUM> may associate and hand-off a queued action to an engine that will execute the action. The dispatcher <NUM> may control routing of each queued action to a particular one of the applications servers <NUM> based on load balancing and other optimization considerations. The dispatcher <NUM> may receive an instruction from the resource manager <NUM> to provision new nodes when the current computing resources are at or above a threshold capacity. The dispatcher <NUM> also may receive an instruction from the resource manager to release nodes when the current computing resources are at or below a threshold capacity. The dispatcher <NUM> accordingly may instruct the cluster manager <NUM> to dynamically provision new nodes or release existing nodes based on demand for computing resources. The nodes may be computing nodes or storage nodes in connection with the applications servers <NUM>, the relational databases <NUM>, and the key/value stores <NUM>.

The resource manager <NUM> may monitor the action queue <NUM>. The resource manager <NUM> also may monitor the current load on the applications servers <NUM> to determine the availability of resources to execute the queued actions. Based on the monitoring, the resource manager may communicate, through the dispatcher <NUM>, with the cluster manager <NUM> to request dynamic allocation and de-allocation of nodes.

The cluster manager <NUM> may be a distributed entity that manages all of the nodes of the applications servers <NUM>. The cluster manager <NUM> may dynamically provision new nodes or release existing nodes based on demand for computing resources. The cluster manager <NUM> may implement a group membership services protocol. The cluster manager <NUM> also may perform a task monitoring function. The task monitoring function may involve tracking resource usage, such as CPU utilization, the amount of data read/written, storage size, etc..

The applications servers <NUM> may perform processes that manage or host analytic server execution, data requests, etc. The engines provided by the energy management platform <NUM>, such as the engines that perform data services, batch processing, stream services, may be hosted within the applications servers <NUM>. The engines are discussed in more detail herein.

In an embodiment, the applications servers <NUM> may be part of a computer cluster of a plurality of loosely or tightly connected computers that are coordinated to work as a system in performing the services and applications of the energy management platform <NUM>. The nodes (e.g., servers) of the cluster may be connected to each other through fast local area networks ("LAN"), with each node running its own instance of an operating system. The applications servers <NUM> may be implemented as a computer cluster to improve performance and availability over that of a single computer, while typically being more cost-effective than single computers of comparable speed or availability. The applications servers <NUM> may be software, hardware, or a combination of both.

The relational databases <NUM> may maintain various data supporting the energy management platform <NUM>. In an embodiment, non-time series data may be stored in the relational databases <NUM>, as discussed in more detail herein.

The key/value stores <NUM> may maintain various data supporting the energy management platform <NUM>. In an embodiment, time series data (e.g., meter readings, meter events, etc.) may be stored in the key/value store, as discussed in more detail herein. In an embodiment, the key/value stores <NUM> may be implemented with Apache Cassandra, an open source distributed database management system designed to handle large amounts of data across a multitude of commodity servers. In an embodiment, other database management systems for key/value stores may be used.

In an embodiment, one or more of the applications servers <NUM>, the relational databases <NUM>, and the key/value stores <NUM> may be implemented by the entity that owns, maintains, or controls the energy management platform <NUM>.

In an embodiment, one or more of the applications servers <NUM>, the relational databases <NUM>, and the key/value stores <NUM> may be implemented by a third party that may provide a computing environment for lease to the entity that owns, maintains, or controls the energy management platform <NUM>. In an embodiment, the applications servers <NUM>, the relational databases <NUM>, and the key/value stores <NUM> implemented by the third party may communicate with the energy management platform <NUM> through a network, such as the network <NUM> of <FIG>.

The computing environment provided by the third party for the entity that owns, maintains, or controls the energy management platform <NUM> may be a cloud computing platform that allows the entity that owns, maintains, or controls the energy management platform <NUM> to rent virtual computers on which to run its own computer applications. Such applications may include, for example, the applications performed by the applications servers <NUM>, as discussed in more detail herein. In an embodiment, the computing environment may allow a scalable deployment of applications by providing a web service through which the entity that owns, maintains, or controls the energy management platform <NUM> can boot a virtual appliance used to create a virtual machine containing any software desired. In an embodiment, the entity that owns, maintains, or controls the energy management platform <NUM> may create, launch, and terminate server instances as needed, paying based on time usage time, data usage, or any combination of these or other factors. The ability to provision and release computing resources in this manner supports the ability of the energy management platform <NUM> to dynamically scale according to the demand on the energy management platform <NUM>.

<FIG> illustrates an example applications server <NUM> of an energy management platform, in accordance with an embodiment of the present disclosure. In an embodiment, one or more of the applications servers <NUM> of <FIG> may be implemented with applications server <NUM> of <FIG>. The applications server <NUM> includes a data integrator (data loading) module <NUM>, an integration services module <NUM>, a data services module <NUM>, a computational services module <NUM>, a stream analytic services module <NUM>, a batch parallel processing analytic services module <NUM>, a normalization module <NUM>, an analytics container <NUM>, a data model <NUM>, and a user interface (UI) services module <NUM>. In some embodiments, the applications server <NUM> can also include a cybersecurity analysis module <NUM>. In some cases, the cybersecurity analysis module <NUM> can be implemented as the cybersecurity analysis module <NUM> of <FIG>.

In some embodiments, the analytics platform supported by the applications server <NUM> includes multiple services that each handles a specific data management or analysis capability. The services include the data integrator module <NUM>, the integration services module <NUM>, the data services module <NUM>, the computational services module <NUM>, the stream analytic services module <NUM>, batch parallel processing analytic services module <NUM>, and the UI services module <NUM>. All or some services within the analytics platform may be modular and accordingly architected specifically to execute their respective capabilities for large data volumes and at high speed. The services may be optimized in software for high performance distributed computing over a computer cluster including the applications servers <NUM>.

The modules and components of the applications server <NUM> in <FIG> and all the figures herein are merely exemplary, and may be variously combined into fewer modules and components, or separated into additional modules and components. The described functionality of the modules and components may be performed by other modules and components.

<FIG> illustrates an example machine <NUM> within which a set of instructions for causing the machine to perform one or more of the embodiments described herein can be executed, in accordance with an embodiment of the present disclosure. The machine may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment.

The machine <NUM> includes a processor <NUM> (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both), a main memory <NUM>, and a nonvolatile memory <NUM> (e.g., volatile RAM and non-volatile RAM), which communicate with each other via a bus <NUM>. In some cases, the example machine <NUM> can correspond to, include, or be included within a computing device or system. For example, in some embodiments, the machine <NUM> can be a desktop computer, a laptop computer, personal digital assistant (PDA), an appliance, a wearable device, a camera, a tablet, or a mobile phone, etc. In one embodiment, the machine <NUM> also includes a video display <NUM>, an alphanumeric input device <NUM> (e.g., a keyboard), a cursor control device <NUM> (e.g., a mouse), a drive unit <NUM>, a signal generation device <NUM> (e.g., a speaker) and a network interface device <NUM>.

In one embodiment, the video display <NUM> includes a touch sensitive screen for user input. In one embodiment, the touch sensitive screen is used instead of a keyboard and mouse. The disk drive unit <NUM> includes a machine-readable medium <NUM> on which is stored one or more sets of instructions <NUM> (e.g., software) embodying any one or more of the methodologies or functions described herein. The instructions <NUM> can also reside, completely or at least partially, within the main memory <NUM> and/or within the processor <NUM> during execution thereof by the computer system <NUM>. The instructions <NUM> can further be transmitted or received over a network <NUM> via the network interface device <NUM>. In some embodiments, the machine-readable medium <NUM> also includes a database <NUM>.

Volatile RAM may be implemented as dynamic RAM (DRAM), which requires power continually in order to refresh or maintain the data in the memory. Non- volatile memory is typically a magnetic hard drive, a magnetic optical drive, an optical drive (e.g., a DVD RAM), or other type of memory system that maintains data even after power is removed from the system. The non-volatile memory may also be a random access memory. The non-volatile memory can be a local device coupled directly to the rest of the components in the data processing system. A non-volatile memory that is remote from the system, such as a network storage device coupled to any of the computer systems described herein through a network interface such as a modem or Ethernet interface, can also be used.

While the machine-readable medium <NUM> is shown in an exemplary embodiment to be a single medium, the term "machine-readable medium" should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term "machine-readable medium" shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure. The term "machine-readable medium" shall accordingly be taken to include, but not be limited to, solid-state memories, optical and magnetic media, and carrier wave signals. The term "storage module" as used herein may be implemented using a machine-readable medium.

In general, the routines executed to implement the embodiments of the present disclosure can be implemented as part of an operating system or a specific application, component, program, object, module or sequence of instructions referred to as "programs" or "applications". For example, one or more programs or applications can be used to execute specific processes described herein. The programs or applications typically comprise one or more instructions set at various times in various memory and storage devices in the machine and that, when read and executed by one or more processors, cause the machine to perform operations to execute elements involving the various aspects of the embodiments described herein.

The executable routines and data may be stored in various places, including, for example, ROM, volatile RAM, non-volatile memory, and/or cache. Portions of these routines and/or data may be stored in any one of these storage devices. Further, the routines and data can be obtained from centralized servers or peer-to-peer networks. Different portions of the routines and data can be obtained from different centralized servers and/or peer-to-peer networks at different times and in different communication sessions, or in a same communication session. The routines and data can be obtained in entirety prior to the execution of the applications. Alternatively, portions of the routines and data can be obtained dynamically, just in time, when needed for execution. Thus, it is not required that the routines and data be on a machine-readable medium in entirety at a particular instance of time.

While embodiments have been described fully in the context of machines, those skilled in the art will appreciate that the various embodiments are capable of being distributed as a program product in a variety of forms, and that the embodiments described herein apply equally regardless of the particular type of machine- or computer-readable media used to actually effect the distribution. Examples of machine-readable media include, but are not limited to, recordable type media such as volatile and non-volatile memory devices, floppy and other removable disks, hard disk drives, optical disks (e.g., Compact Disk Read-Only Memory (CD ROMS), Digital Versatile Disks, (DVDs), etc.), among others, and transmission type media such as digital and analog communication links.

Alternatively, or in combination, the embodiments described herein can be implemented using special purpose circuitry, with or without software instructions, such as using Application-Specific Integrated Circuit (ASIC) or Field-Programmable Gate Array (FPGA). Embodiments can be implemented using hardwired circuitry without software instructions, or in combination with software instructions. Thus, the techniques are limited neither to any specific combination of hardware circuitry and software, nor to any particular source for the instructions executed by the data processing system.

For purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the description. It will be apparent, however, to one skilled in the art that embodiments of the disclosure can be practiced without these specific details. In some instances, modules, structures, processes, features, and devices are shown in block diagram form in order to avoid obscuring the description. In other instances, functional block diagrams and flow diagrams are shown to represent data and logic flows. The components of block diagrams and flow diagrams (e.g., modules, engines, blocks, structures, devices, features, etc.) may be variously combined, separated, removed, reordered, and replaced in a manner other than as expressly described and depicted herein.

Reference in this specification to "one embodiment", "an embodiment", "other embodiments", "another embodiment", or the like means that a particular feature, design, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. The appearances of, for example, the phrases "according to an embodiment", "in one embodiment", "in an embodiment", or "in another embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, whether or not there is express reference to an "embodiment" or the like, various features are described, which may be variously combined and included in some embodiments but also variously omitted in other embodiments. Similarly, various features are described which may be preferences or requirements for some embodiments but not other embodiments.

Although embodiments have been described with reference to specific exemplary embodiments, it will be evident that the various modifications and changes can be made to these embodiments. Accordingly, the specification and drawings are to be regarded in an illustrative sense rather than in a restrictive sense. The foregoing specification provides a description with reference to specific exemplary embodiments. It will be evident that various modifications can be made thereto without departing from the broader scope as set forth in the following claims. The specification and drawings are, accordingly, to be regarded in an illustrative sense rather than a restrictive sense.

Although some of the drawings illustrate a number of operations or method steps in a particular order, steps that are not order dependent may be reordered and other steps may be combined or omitted. While some reordering or other groupings are specifically mentioned, others will be apparent to those of ordinary skill in the art and so do not present an exhaustive list of alternatives. Moreover, it should be recognized that the stages could be implemented in hardware, firmware, software or any combination thereof.

It should also be understood that a variety of changes may be made without departing from the essence of the present disclosure. Such changes are also implicitly included in the description. They still fall within the scope of the present disclosure. It should be understood that this disclosure is intended to yield a patent covering numerous aspects of the disclosed technology, both independently and as an overall system, and in both method and apparatus modes.

Claim 1:
A computer-implemented method, comprising:
(a) obtaining (<NUM>) a first set of data comprising detected network traffic in one or more network components within an energy delivery network and a second set of data (<NUM>) comprising at least one of customer data, operations data, or economic data relating to the energy delivery network;
(b) generating (<NUM>), using the first set of data, a first metric indicative of a likelihood that a selected network component within the energy delivery network is affected by one or more cyber vulnerabilities, wherein generating the first metric comprises utilizing an advanced behavioral indicator associated with the detected network traffic, wherein the advanced behavioral indicator is based on analysis of at least one of a multiple-step series of activities associated with the detected network traffic or a combination of multiple indicators associated with the detected network traffic;
(c) generating (<NUM>), using the second set of data, a second metric indicative of a calculated impact to the energy delivery network or a portion thereof due to the one or more cyber vulnerabilities affecting the selected network component; and
(d) providing the first metric and the second metric as inputs to a machine learning algorithm configured to generate an overall cybersecurity risk level associated with the selected network component.