Patent ID: 12192175

DESCRIPTION OF EXAMPLE EMBODIMENTS

Overview

According to one or more embodiments of the disclosure, a device in a network identifies a packet sent via the network towards an endpoint as being a control packet for the endpoint. The device extracts one or more control parameter values from the control packet. The device compares the one or more control parameter values to a policy associated with the endpoint. The device initiates a corrective measure, based on a determination that the one or more control parameter values violate the policy associated with the endpoint.

Description

A computer network is a geographically distributed collection of nodes interconnected by communication links and segments for transporting data between end nodes, such as personal computers and workstations, or other devices, such as sensors, etc. Many types of networks are available, ranging from local area networks (LANs) to wide area networks (WANs). LANs typically connect the nodes over dedicated private communications links located in the same general physical location, such as a building or campus. WANs, on the other hand, typically connect geographically dispersed nodes over long-distance communications links, such as common carrier telephone lines, optical lightpaths, synchronous optical networks (SONET), synchronous digital hierarchy (SDH) links, or Powerline Communications, and others. Other types of networks, such as field area networks (FANs), neighborhood area networks (NANs), personal area networks (PANs), etc. may also make up the components of any given computer network.

In various embodiments, computer networks may include an Internet of Things network. Loosely, the term “Internet of Things” or “IoT” (or “Internet of Everything” or “IoE”) refers to uniquely identifiable objects (things) and their virtual representations in a network-based architecture. In particular, the IoT involves the ability to connect more than just computers and communications devices, but rather the ability to connect “objects” in general, such as lights, appliances, vehicles, heating, ventilating, and air-conditioning (HVAC), windows and window shades and blinds, doors, locks, etc. The “Internet of Things” thus generally refers to the interconnection of objects (e.g., smart objects), such as sensors and actuators, over a computer network (e.g., via IP), which may be the public Internet or a private network.

Often, IoT networks operate within a shared-media mesh networks, such as wireless or Powerline Communication networks, etc., and are often on what is referred to as Low-Power and Lossy Networks (LLNs), which are a class of network in which both the routers and their interconnect are constrained. That is, LLN devices/routers typically operate with constraints, e.g., processing power, memory, and/or energy (battery), and their interconnects are characterized by, illustratively, high loss rates, low data rates, and/or instability. IoT networks are comprised of anything from a few dozen to thousands or even millions of devices, and support point-to-point traffic (between devices inside the network), point-to-multipoint traffic (from a central control point such as a root node to a subset of devices inside the network), and multipoint-to-point traffic (from devices inside the network towards a central control point).

Fog computing is a distributed approach of cloud implementation that acts as an intermediate layer from local networks (e.g., IoT networks) to the cloud (e.g., centralized and/or shared resources, as will be understood by those skilled in the art). That is, generally, fog computing entails using devices at the network edge to provide application services, including computation, networking, and storage, to the local nodes in the network, in contrast to cloud-based approaches that rely on remote data centers/cloud environments for the services. To this end, a fog node is a functional node that is deployed close to fog endpoints to provide computing, storage, and networking resources and services. Multiple fog nodes organized or configured together form a fog system, to implement a particular solution. Fog nodes and fog systems can have the same or complementary capabilities, in various implementations. That is, each individual fog node does not have to implement the entire spectrum of capabilities. Instead, the fog capabilities may be distributed across multiple fog nodes and systems, which may collaborate to help each other to provide the desired services. In other words, a fog system can include any number of virtualized services and/or data stores that are spread across the distributed fog nodes. This may include a master-slave configuration, publish-subscribe configuration, or peer-to-peer configuration.

Low power and Lossy Networks (LLNs), e.g., certain sensor networks, may be used in a myriad of applications such as for “Smart Grid” and “Smart Cities.” A number of challenges in LLNs have been presented, such as:1) Links are generally lossy, such that a Packet Delivery Rate/Ratio (PDR) can dramatically vary due to various sources of interferences, e.g., considerably affecting the bit error rate (BER);2) Links are generally low bandwidth, such that control plane traffic must generally be bounded and negligible compared to the low rate data traffic;3) There are a number of use cases that require specifying a set of link and node metrics, some of them being dynamic, thus requiring specific smoothing functions to avoid routing instability, considerably draining bandwidth and energy;4) Constraint-routing may be required by some applications, e.g., to establish routing paths that will avoid non-encrypted links, nodes running low on energy, etc.;5) Scale of the networks may become very large, e.g., on the order of several thousands to millions of nodes; and6) Nodes may be constrained with a low memory, a reduced processing capability, a low power supply (e.g., battery).

In other words, LLNs are a class of network in which both the routers and their interconnect are constrained: LLN routers typically operate with constraints, e.g., processing power, memory, and/or energy (battery), and their interconnects are characterized by, illustratively, high loss rates, low data rates, and/or instability. LLNs are comprised of anything from a few dozen and up to thousands or even millions of LLN routers, and support point-to-point traffic (between devices inside the LLN), point-to-multipoint traffic (from a central control point to a subset of devices inside the LLN) and multipoint-to-point traffic (from devices inside the LLN towards a central control point).

An example implementation of LLNs is an “Internet of Things” network. Loosely, the term “Internet of Things” or “IoT” may be used by those in the art to refer to uniquely identifiable objects (things) and their virtual representations in a network-based architecture. In particular, the next frontier in the evolution of the Internet is the ability to connect more than just computers and communications devices, but rather the ability to connect “objects” in general, such as lights, appliances, vehicles, HVAC (heating, ventilating, and air-conditioning), windows and window shades and blinds, doors, locks, etc. The “Internet of Things” thus generally refers to the interconnection of objects (e.g., smart objects), such as sensors and actuators, over a computer network (e.g., IP), which may be the Public Internet or a private network. Such devices have been used in the industry for decades, usually in the form of non-IP or proprietary protocols that are connected to IP networks by way of protocol translation gateways. With the emergence of a myriad of applications, such as the smart grid advanced metering infrastructure (AMI), smart cities, and building and industrial automation, and cars (e.g., that can interconnect millions of objects for sensing things like power quality, tire pressure, and temperature and that can actuate engines and lights), it has been of the utmost importance to extend the IP protocol suite for these networks.

FIG.1is a schematic block diagram of an example simplified computer network100illustratively comprising nodes/devices at various levels of the network, interconnected by various methods of communication. For instance, the links may be wired links or shared media (e.g., wireless links, powerline communication links, etc.) where certain nodes, such as, e.g., routers, sensors, computers, etc., may be in communication with other devices, e.g., based on connectivity, distance, signal strength, current operational status, location, etc.

Specifically, as shown in the example IoT network100, three illustrative layers are shown, namely cloud layer110, fog layer120, and IoT device layer130. Illustratively, the cloud110may comprise general connectivity via the Internet112, and may contain one or more datacenters114with one or more centralized servers116or other devices, as will be appreciated by those skilled in the art. Within the fog layer120, various fog nodes/devices122(e.g., with fog modules, described below) may execute various fog computing resources on network edge devices, as opposed to datacenter/cloud-based servers or on the endpoint nodes132themselves of the IoT layer130. For example, fog nodes/devices122may include edge routers and/or other networking devices that provide connectivity between cloud layer110and IoT device layer130. Data packets (e.g., traffic and/or messages sent between the devices/nodes) may be exchanged among the nodes/devices of the computer network100using predefined network communication protocols such as certain known wired protocols, wireless protocols, powerline communication protocols, or other shared-media protocols where appropriate. In this context, a protocol consists of a set of rules defining how the nodes interact with each other.

Those skilled in the art will understand that any number of nodes, devices, links, etc. may be used in the computer network, and that the view shown herein is for simplicity. Also, those skilled in the art will further understand that while the network is shown in a certain orientation, the network100is merely an example illustration that is not meant to limit the disclosure.

Data packets (e.g., traffic and/or messages) may be exchanged among the nodes/devices of the computer network100using predefined network communication protocols such as certain known wired protocols, wireless protocols (e.g., IEEE Std. 802.15.4, Wi-Fi, Bluetooth®, DECT-Ultra Low Energy, LoRa, etc.), powerline communication protocols, or other shared-media protocols where appropriate. In this context, a protocol consists of a set of rules defining how the nodes interact with each other.

FIG.2is a schematic block diagram of an example node/device200that may be used with one or more embodiments described herein, e.g., as any of the nodes or devices shown inFIG.1above or described in further detail below. The device200may comprise one or more network interfaces210(e.g., wired, wireless, etc.), at least one processor220, and a memory240interconnected by a system bus250, as well as a power supply260(e.g., battery, plug-in, etc.).

Network interface(s)210include the mechanical, electrical, and signaling circuitry for communicating data over links coupled to the network. The network interfaces210may be configured to transmit and/or receive data using a variety of different communication protocols, such as TCP/IP, UDP, etc. Note that the device200may have multiple different types of network connections210, e.g., wireless and wired/physical connections, and that the view herein is merely for illustration. Also, while the network interface210is shown separately from power supply260, for powerline communications the network interface210may communicate through the power supply260, or may be an integral component of the power supply. In some specific configurations the powerline communication signal may be coupled to the power line feeding into the power supply.

The memory240comprises a plurality of storage locations that are addressable by the processor(s)220and the network interfaces210for storing software programs and data structures associated with the embodiments described herein. The processor220may comprise necessary elements or logic adapted to execute the software programs and manipulate the data structures245. An operating system242(e.g., the Internetworking Operating System, or IOS®, of Cisco Systems, Inc., another operating system, etc.), portions of which are typically resident in memory240and executed by the processor(s), functionally organizes the node by, inter alia, invoking network operations in support of software processors and/or services executing on the device. These software processors and/or services may comprise a network security process248.

It will be apparent to those skilled in the art that other processor and memory types, including various computer-readable media, may be used to store and execute program instructions pertaining to the techniques described herein. Also, while the description illustrates various processes, it is expressly contemplated that various processes may be embodied as modules configured to operate in accordance with the techniques herein (e.g., according to the functionality of a similar process). Further, while processes may be shown and/or described separately, those skilled in the art will appreciate that processes may be routines or modules within other processes.

In general, network security process248may be configured to perform any or all of the following tasks:1. Identifying and classifying devices in the network—this may entail, for example, determining the make, model, software configuration, type, etc. of a given device.2. Discerning operational insights about a device—for example, network security process248may assess the traffic of a particular device, to determine what the device is doing, or attempting to do, via the network. Such information may take the form of device details and communication maps for the device. In further cases, the device functions and application flows may be converted into tags and/or events for presentation to a user interface. Further, process248may also track variable changes, to monitor the integrity of the industrial workflow.3. Detecting anomalies—network security process248may also assess the behaviors of a device on the network, to determine whether its behaviors are anomalous. In various embodiments, this may entail network security process248determining whether the behavior of the device has changed significantly over time and/or does not fit the expected behavioral pattern for its classification. For example, if the device is identifies as being a temperature sensor that periodically sends temperature measurements to a supervisory service, but the device is instead communicating data elsewhere, process248may deem this behavior anomalous.

In various embodiments, network security process248may employ any number of machine learning techniques, to assess the gathered telemetry data regarding the traffic of the device. In general, machine learning is concerned with the design and the development of techniques that receive empirical data as input (e.g., telemetry data regarding traffic in the network) and recognize complex patterns in the input data. For example, some machine learning techniques use an underlying model M, whose parameters are optimized for minimizing the cost function associated to M, given the input data. For instance, in the context of classification, the model M may be a straight line that separates the data into two classes (e.g., labels) such that M=a*x+b*y+c and the cost function is a function of the number of misclassified points. The learning process then operates by adjusting the parameters a,b,c such that the number of misclassified points is minimal. After this optimization/learning phase, network security process248can use the model M to classify new data points, such as information regarding new traffic flows in the network. Often, M is a statistical model, and the cost function is inversely proportional to the likelihood of M, given the input data.

In various embodiments, network security process248may employ one or more supervised, unsupervised, or semi-supervised machine learning models. Generally, supervised learning entails the use of a training set of data, as noted above, that is used to train the model to apply labels to the input data. For example, the training data may include sample telemetry data that is “normal,” or “suspicious.” On the other end of the spectrum are unsupervised techniques that do not require a training set of labels. Notably, while a supervised learning model may look for previously seen attack patterns that have been labeled as such, an unsupervised model may instead look to whether there are sudden changes in the behavior of the network traffic. Semi-supervised learning models take a middle ground approach that uses a greatly reduced set of labeled training data.

Example machine learning techniques that network security process248can employ may include, but are not limited to, nearest neighbor (NN) techniques (e.g., k-NN models, replicator NN models, etc.), statistical techniques (e.g., Bayesian networks, etc.), clustering techniques (e.g., k-means, mean-shift, etc.), neural networks (e.g., reservoir networks, artificial neural networks, etc.), support vector machines (SVMs), logistic or other regression, Markov models or chains, principal component analysis (PCA) (e.g., for linear models), multi-layer perceptron (MLP) ANNs (e.g., for non-linear models), replicating reservoir networks (e.g., for non-linear models, typically for time series), random forest classification, or the like.

The performance of a machine learning model can be evaluated in a number of ways based on the number of true positives, false positives, true negatives, and/or false negatives of the model. For example, the false positives of the model may refer to the number of traffic flows that are incorrectly classified as malware-generated, anomalous, etc. Conversely, the false negatives of the model may refer to the number of traffic flows that the model incorrectly classifies as normal, when actually malware-generated, anomalous, etc. True negatives and positives may refer to the number of traffic flows that the model correctly classifies as normal or malware-generated, etc., respectively. Related to these measurements are the concepts of recall and precision. Generally, recall refers to the ratio of true positives to the sum of true positives and false negatives, which quantifies the sensitivity of the model. Similarly, precision refers to the ratio of true positives the sum of true and false positives.

In some cases, network security process248may assess the captured telemetry data on a per-flow basis. In other embodiments, network security process248may assess telemetry data for a plurality of traffic flows based on any number of different conditions. For example, traffic flows may be grouped based on their sources, destinations, temporal characteristics (e.g., flows that occur around the same time, etc.), combinations thereof, or based on any other set of flow characteristics.

As noted above, the very nature of the IoT presents certain challenges, from a security standpoint. Indeed, the diversity of the various devices in the network in terms of their hardware, software, and purposes (e.g., sensing, controlling, etc.), as well as the specific configuration of the network (e.g., cells in an industrial network, etc.), can make enforcing network security particularly challenging.

Best practices for Industrial IoT security typically follow standardized models, such as IEC 62443. This security model implements both operational technology (OT) and information technology (IT) security levels and establishes how security should be designed in industrial systems. Furthermore, it describes how security between levels is accomplished through the use of controlled conduits. However, industrial security remains very difficult to enforce, as evidenced by recent industrial attacks where this model was in place. A superior approach would be to leverage intent-based networking, complete with abstraction, automation and analytics, to define, enforce and assure IoT security policies.

It is also important to recognize that IoT devices typically follow a very well prescribed communication profile (e.g., to which devices they should be communicating, on what protocol, and what the protocol should be doing). For instance, a supervisory control and data acquisition (SCADA) slave should only ever communicate to a SCADA master on an established port and should only execute allowable commands. However, it remains very difficult to both 1.) verify that the things, such as intelligent electronic devices, programmable logic controllers (PLCs), variable-frequency drive (VFD), human-machine interfaces (HMIs), input/output (I/O) controllers, etc., are communicating in the expected way and 2.) control their behaviors such that any unexpected network attacks are isolated.

Even when the communications between endpoints are seemingly innocuous, there has been a recent trend in malware taking advantage of these communications to mo damage equipment. In these forms of attacks, an infected endpoint can send control commands to another endpoint, with whom communication is allowed, that can damage or disrupt the operations of the equipment and, potentially, the industrial environment as a whole. For example, malicious SCADA commands to a PLC could cause the PLC to drive a motor in an unsafe way, cause power to be turned off or on to a circuit (e.g., a feeder in an electrical power station), or the like.

Intent-Based Security for Industrial IoT Devices

The techniques herein introduce a network architecture implementing intent-based security for an IoT network, such as an industrial IoT network. In some aspects, the proposed architecture takes a holistic approach to network security that relies on information and processing from a number of different sources in the network, such as industrial firewalls, lightweight telemetry sensors, and services such as authentication, authorization, and accounting (AAA) services, and the like.

Illustratively, the techniques described herein may be performed by hardware, software, and/or firmware, such as in accordance with the network security process248, which may include computer executable instructions executed by the processor220(or independent processor of interfaces210) to perform functions relating to the techniques described herein.

Specifically, according to various embodiments, a device in a network identifies a packet sent via the network towards an endpoint as being a control packet for the endpoint. The device extracts one or more control parameter values from the control packet. The device compares the one or more control parameter values to a policy associated with the endpoint. The device initiates a corrective measure, based on a determination that the one or more control parameter values violate the policy associated with the endpoint.

Operationally,FIG.3illustrates an example network architecture300for an industrial network, according to various embodiments. As shown, architecture300may include industrial equipment304connected to a controller306, such as a PLC, a VFD, or the like, that controls the operations of industrial equipment304. In turn, controller306for industrial equipment304may be connected to an HMI310via networking equipment308, allowing a human user to interface with it (e.g., to visualize the industrial process, issue commands, etc.). In addition, networking equipment308may also provide connectivity via the greater network302to any number of network services312-320provided in the local network of networking equipment308and/or remotely. For example, services312-320may be implemented in the local network via dedicated equipment or virtualized across any number of devices (e.g., networking equipment308). In other cases, services312-320may be provided by servers in a remote data center, the cloud, or the like.

As would be appreciated, industrial equipment304may differ, depending on the industrial setting in which architecture300is implemented. In many cases, industrial equipment304may comprise an actuator such as, but not limited to, a motor, a pump, a solenoid, or the like. In other cases, industrial equipment304may include a circuit and controller306may control the powering of the circuit.

Industrial equipment304may also include any number of sensors configured to take measurements regarding the physical process implemented by industrial equipment304. For example, such sensors may take temperature readings, distance measurements, humidity readings, voltage or amperage measurements, or the like, and provide them to controller306for industrial equipment304. During operation, controller306may use the sensor data from industrial equipment304as part of a control loop, thereby allowing controller306to adjust the industrial process as needed.

HMI310may include a dedicated touch screen display or may take the form of a workstation, portable tablet or other handheld, or the like. Thus, during operation, visualization data may be provided to HMI310regarding the industrial process performed by industrial equipment304. For example, such visualizations may include a graphical representation of the industrial process (e.g., the filling of a tank, etc.), the sensor data from industrial equipment304, the control parameter values used by controller306, or the like. In some embodiments, HMI310may also allow for the reconfiguration of controller306, such as by adjusting its control parameters for industrial equipment304(e.g., to shut down the industrial process, etc.).

Networking equipment308may include any number of switches, routers, firewalls, telemetry exporters and/or collectors, gateways, bridges, and the like. In some embodiments, these networking functions may be performed in a virtualized/containerized manner. For example, a telemetry exporter may take the form of a containerized application installed to networking equipment308, to collect and export telemetry regarding the operation networking equipment308(e.g., queue state information, memory or processor resource utilization, etc.) and/or network302(e.g., measured delays, drops, jitter, etc.).

In some embodiments, at least a portion of network302may be implemented as a software-defined network (SDN). In such implementations, control plane decisions by the networking equipment of network302, such as networking equipment308, may be centralized with an SDN controller. For example, rather than networking equipment308establishing routing paths and making other control decisions, individually, such decisions can be centralized with an SDN controller (e.g., network supervisory service312, etc.).

During operation, network supervisory service312may function to monitor the status and health of network302and networking equipment308. An example of such a network supervisory service is DNA-Center by Cisco Systems, Inc. For example, in some implementations, network supervisory service312may take the form of a network assurance service that assesses the health of network302and networking equipment308through the use of heuristics, rules, and/or machine learning models. In some cases, this monitoring can also be predictive in nature, allowing network supervisory service312to predict failures and other network conditions before they actually occur. In either case, network supervisory service312may also provide control over network302, such as by reconfiguring networking equipment308, adjusting routing in network302, and the like. As noted above, network supervisory service312may also function as an SDN controller for networking equipment308, in some embodiments.

As shown, architecture300may also include SCADA service314which supervises the operation of the industrial process. More specifically, SCADA service314is may communicate with controller306, to receive data regarding the industrial process (e.g., sensor data from industrial equipment304, etc.) and provide control over controller306, such as by pushing new control routines, software updates, and the like, to controller306.

As would be appreciated, SCADA service314, controller306, and/or HMI310may communicate using an automation protocol. Examples of such protocols may include, but are not limited to, Profibus, Modbus, DeviceNet, HART, DNP3, IEC 61850, IEC 60870-5, and the like. In addition, different protocols may be used within network102and among networking equipment308, depending on the specific implementation of architecture300. Further, different portions of network302may be organized into different cells or other segmented areas that are distinct from one another and interlinked via networking equipment308.

Architecture300may also include a policy service316that is responsible for creating and managing security and access policies for endpoints in network302. An example of such a policy service316is the Identity Services Engine (ISE) by Cisco Systems, Inc. In various embodiments, as detailed below, policy service316may also be configured to identify the types of endpoints present in network302(e.g., HMI310, controller306, etc.) and their corresponding actions/functions. In turn, this information can be used to drive the policies that policy service316creates.

Security service318is configured to enforce the various policies created and curated by policy service316in the network. For example, such policies may be implemented by security service318as access control lists (ACLs), firewall rules, or the like, that are distributed to networking equipment308for enforcement.

According to various embodiments, architecture300may also include asset inventory service320that is used to collect information about learned assets/endpoints in network302and maintain an inventory of these various devices in network302. In various embodiments, asset inventory service320may do so by embedding sensing modules in networking equipment308which passively analyze communications between endpoints. The sensors may use deep packet inspection (DPI) to not only identify the protocols in use by a given packet (e.g., the automation protocol used between HMI310, controller306, and SCADA service314), but also understand the action(s) that are being communicated and to classify both the type of device/component and its application behavior.

For example, when a sensor module executed by networking equipment308identifies the use of an automation protocol by a packet, it may examine the payload of each flow to identify any or all of the following:The device type (e.g., based on passive scan of traffic and matching a known criterion, the device is classified).The software and/or hardware versions of the device.MAC and IP addresses of all devices with which the discovered device is communicating.The activity profile of the device (e.g., how is it trying to communicate), and the protocol(s) it is using.The commands that are being passed (e.g., SCADA commands, etc.), down to the specific control parameter values.

The sensor modules of networking equipment308then then organize the collected information into meaningful tags. In general, these tags are simply a way to categorize devices and their behaviors, similar to the same way a human may look at a pen or a pencil and categorize them as writing instruments. Each device can also have multiple tags associated with it, such as the following:Component Tags—these tags identify device specific details (e.g., Device ID, SCADA station, PLC, Windows device, etc.).Activity Tags—these tags identify what the device is doing at the protocol level (Programming CPU, Heartbeat, Emergency Break, Data Push).User-Defined Tags—these could be custom tags to supply additional context (e.g. “Cell1Tag”).Dynamically Generated Tags these could be added dynamically (e.g., using ML) to signify whether the behavior of the device is normal or anomalous, or for other dynamic conditions.Scalable Group Tags—These tags are applied to specific packet flows between a defined group of devices/services in the network. For example, in the case shown, HMI310, controller306, and SCADA service314may be tagged as belonging to a particular group.

The sensor modules embedded in networking equipment308may also collect metadata about the communicating devices/endpoints, including its network identifiers (e.g., IP and MAC addresses), vendor, device-type, firmware version, the switch ID and port where the device is connected, etc. As the sensor module learns details of a new device/endpoint in network302, it may send its collected metadata about that device, along with its tags, to the asset inventory service320.

In this manner, asset inventory service320may maintain an inventory of each of the endpoint devices in network302, their associated tags, and their metadata. Thus, as new devices are discovered in network302, their profile information is added to the live inventory of devices maintained by asset inventory service320. As noted above, the various tags applied by the sensor modules deployed to networking equipment308and used by asset inventory service320may be predefined or may, via a user interface (not show) be user-defined.

FIGS.4A-4Billustrate example displays400,410, respectively, showing component and activity tags, in some embodiments. As shown, the various component tags can be used to identify a particular endpoint or other device in the network by its type (e.g., PLC, SCADA station, etc.), its software (e.g., CodeSys, Windows, etc.). In addition, analysis of the traffic of the device can also lead to various activity tags being applied to that device, as well. For example, such activity tags may distinguish between control system behaviors (e.g., insert program, device init., etc.) and IT behaviors (e.g., host config., ping, etc.).

Referring again toFIG.3, to facilitate the labeling of devices in network302using tags, asset inventory service320may also leverage device classification functions provided by policy service316, to identify the component and activity tags of a particular device in network302under scrutiny. In general, device classification (also known as “device profiling”) has traditionally used static rules and heuristics for the determination. In further embodiments, the device classification by policy service316can be achieved by applying a trained machine learning-based classifier to the captured telemetry data from networking equipment308. Such telemetry data can also take the form of information captured through active and/or passive probing of the device. Notably, this probing may entail policy service316sending any or all of the following probes via networking equipment308:Dynamic Host Configuration Protocol (DHCP) probes with helper addressesSPAN probes, to get messages in INIT-REBOOT and SELECTING states, use of ARP cache for IP/MAC binding, etc.Netflow probesHyperText Transfer Protocol (HTTP) probes to obtain information such as the operating system (OS) of the device, Web browser information, etc.Remote Authentication Dial-In User Service (RADIUS) probes.Simple Network Management Protocol (SNMP) to retrieve Management Information Base (MIB) object or receives traps.Domain Name System (DNS) probes to get the Fully Qualified Domain Name (FQDN)etc.

Further information that may be captured by networking equipment308and reported via telemetry data to policy service316may include traffic behavioral characteristics of the traffic of a device, such as the communication protocols used, flow information, timing and pattern data, and the like. In addition, the telemetry data may be indicative of the operational intent of the endpoint device (e.g., controller306, HMI310, etc.).

According to various embodiments, additional information that policy service316and asset inventory service320may use to tag the various devices/components in network302may include any or all of the following:Manufacturer's Usage Description (MUD) information—As proposed in the Internet Engineering Task Force (IETF) draft entitled, “Manufacturer Usage Description Specification,” devices may be configured by their manufacturers to advertise their device specifications. Such information may also indicate the intended communication patterns of the devices.Asset Administration Shell data—this is an Industry 4.0 method to express how an IoT device should behave, including expected communication patterns.IEC 61850 Substation Configuration Language (SCL) data—this is a language that is used primarily in the utility industry to express Intelligent Electronic Device (IED) intent.Open Platform Communication Unified Architecture (OPC UA) data—such data provides industrial models used in manufacturing contexts.

Thus, policy service316, asset inventory service320, and the sensor modules and telemetry exporters of networking equipment308may operate in conjunction with one another to apply various tags to the devices in network302and their traffic flows.

FIG.5illustrates an example screen capture500of an asset profile, in some embodiments. Notably, the techniques herein have been implemented as part of a prototype system and screen capture500is from that prototype system. As can be seen, a particular asset has been identified as a Yokogawa device and has been tagged with various component and activity tags (e.g., PLC, CodeSys, Citect Report, etc.). This profile may be stored by the asset inventory service (e.g., service320inFIG.3) and provide to a user interface, allowing the user to quickly learn information about the device. Such information can also be automatically updated over time, using the techniques herein.

Referring again toFIG.3, policy service316may maintain any number of policies that define the expected behaviors of a given device in network302, in various embodiments. For example, based on the asset profile of controller306, policy service316may determine that controller306is a PLC and associate a policy with controller306only allows HMI310and SCADA service314to communicate with controller306via network302. In turn, policy service316may provide the policy to security service318and pushed to networking equipment308for enforcement.

According to various embodiments, policy service316may also associate application-layer policies to an endpoint device, based on its asset profile. Such policies may specify the set of commands (e.g., control parameter values) that are expected between the device and another particular device in the network. For example, assume that controller306is a PLC that controls a motor. In such a case, the policy may specify that controller306should keep the operating temperature of the industrial process performed by industrial equipment304within a specified temperature range, the rotations per minute (RPMs) of a motor within a specified, or the like. In other cases, the policy may specify that controller should not power a circuit on or off, such as during particular times or on certain days.

Policy service316may enact enforcement of the policies for a particular endpoint in network302(e.g., controller306) via security service318. For example, security service318may translate the policy from policy service316into a set of one or more firewall rules and deploy those rules to a stateful firewall in networking equipment308. In other embodiments, the policy may be implemented by security service318by pushing the policy to a router, switch, telemetry collector, or the like, in networking equipment308, to enforce the policy on any packet traversing that device.

Once the policies have been deployed to networking equipment308, the executing device may analyze each of the packets passing through the device. In turn, it may evaluate whether the packet violates any of the policies and, if so, initiate a corrective measure. For example, the device may block the packet from being delivered and/or raise an alert that a possible attack has been detected (e.g., via security service318).

In the case of a policy that implements a security group, the networking equipment308evaluating a packet may use the policy to validate whether the sender and destination are authorized to communicate with one another. For example, if a packet is sent to controller306by an endpoint other than HMI310or SCADA service314, the networking equipment may determine that this is a policy violation.

According to various embodiments, through the use of DPI or other packet inspect technique, the networking equipment308that receives a packet may also evaluate the protocol(s) in use by the packet. In doing so, it can enforce policies that restrict which protocol(s) are used between endpoints that would otherwise be authorized to communicate with one another. In addition, the policies put into place may further prevent certain actions from being performed with respect to the sender and/or destination of the packet. For example, although one endpoint may generally be authorized by policy to communicate with controller306, it may be restructured from updating the software of controller306.

In further embodiments, when the networking equipment308determines that a given packet is a control packet, it may also enforce any application-level policies to the packet. For example, assume that a packet is sent to controller306from SCADA service314using an automation protocol, it may extract any control parameter value(s) present in the payload of the packet and compare them to the application-level policies associated with controller306. In turn, if the control parameter value(s) violate any of the specified policies, the networking equipment308may block the packet from being delivered to controller306.

By blocking certain control commands send to controller306, based on their control parameter value(s), the networking equipment308can effectively prevent stealthy attacks from being launched in the network against industrial equipment304that are intended to either damage equipment or disrupt the industrial process. For example, by restricting the operating range for industrial equipment304by policy, even seemly innocuous communications can be blocked from reaching controller306and used to control industrial equipment304.

FIG.6illustrates an example simplified procedure for enforcing intent-based security for industrial Internet of Things (IoT) network, in accordance with one or more embodiments described herein. In various embodiments, a non-generic, specifically configured device (e.g., device200) may perform procedure600by executing stored instructions (e.g., process248), such as a router, switch, firewall, or other form of networking equipment in a network through which traffic flows. The procedure600may start at step605, and continues to step610, where, as described in greater detail above, the device may identify a packet sent via the network towards an endpoint as being a control packet for the endpoint. To do so, for example, the device may identify the packet as using an automation protocol.

At step615, as detailed above, the device may extract one or more control parameter values from the control packet. As would be appreciated, the specific format for the payload of the packet may differ, depending on the automation protocol in use, the endpoint, etc. For example, the endpoint the endpoint (e.g., a PLC, a VFD, etc.) controls an actuator and the one or more control parameter values affect how the actuator operates. In another example, the one or more control parameter values affect powering of a circuit, such as in the case of a power station or other industrial setting.

At step620, the device may compare the one or more control parameter values to a policy associated with the endpoint, as described in greater detail above. In various embodiments, such a policy may be based on one or more component tags and one or more activity tags assigned to the endpoint. In general, the policy may define a range or set of one or more values that are acceptable for sending as a control message to the endpoint.

At step625, as detailed above, the device may initiate a corrective measure, based on a determination that the one or more control parameter values violate the policy associated with the endpoint. In one embodiment, the corrective measure may comprise blocking the packet from being delivered to the endpoint. In another embodiment, the corrective measure may entail sending an alert to a user interface, regardless of whether or not the packet is delivered (e.g., a slightly out of range control parameter value may still be delivered, while further out of range values may be blocked entirely). Procedure600then ends at step630.

The techniques described herein, therefore, allow for intent-based security to be implemented in an IoT network. In some aspects, by learning the intent of an endpoint in the network, policies can be put into place that ensure that the endpoint does not deviate from its expected behavior. In further aspects, application-level policies can even be instantiated to prevent control commands from being sent to the endpoint that could lead to the damage of equipment or disruption of the industrial process controlled by the endpoint.

While there have been shown and described illustrative embodiments for intent-based security for industrial IoT devices, it is to be understood that various other adaptations and modifications may be made within the intent and scope of the embodiments herein. For example, while specific models are shown herein for purposes of illustration, other models may be generated in a similar manner, such as with a different number of types of layers. Further, while the techniques herein are described as being performed by certain locations within a network, the techniques herein could also be performed at other locations, as desired (e.g., fully in the cloud, fully within the local network, etc.).

The foregoing description has been directed to specific embodiments. It will be apparent, however, that other variations and modifications may be made to the described embodiments, with the attainment of some or all of their advantages. For instance, it is expressly contemplated that the components and/or elements described herein can be implemented as software being stored on a tangible (non-transitory) computer-readable medium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructions executing on a computer, hardware, firmware, or a combination thereof. Accordingly, this description is to be taken only by way of example and not to otherwise limit the scope of the embodiments herein. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true intent and scope of the embodiments herein.