Patent Description:
Industrial control systems that operate physical systems (e.g., associated with power turbines, jet engines, locomotives, autonomous vehicles, etc.) are increasingly connected to the Internet. As a result, these control systems have been increasingly vulnerable to threats, such as cyber-attacks (e.g., associated with a computer virus, malicious software, etc.) that could disrupt electric power generation and distribution, damage engines, inflict vehicle malfunctions, etc. Current methods primarily consider attack detection in Information Technology ("IT," such as, computers that store, retrieve, transmit, manipulate data) and Operation Technology ("OT," such as direct monitoring devices and communication bus interfaces). Cyber-attacks can still penetrate through these protection layers and reach the physical "domain. " Such attacks can diminish the performance of a control system and may cause total shut down or even catastrophic damage to a plant. In some cases, multiple attacks may occur simultaneously (e.g., more than one actuator, sensor, or parameter inside control system devices might be altered maliciously by an unauthorized party at the same time). Note that some subtle consequences of cyber-attacks, such as stealthy attacks occurring at the domain layer, might not be readily detectable (e.g., when only one monitoring node, such as a sensor node, is used in a detection algorithm). It may also be important to determine when a monitoring node is experiencing a fault (as opposed to a malicious attack) and, in some cases, exactly what type of fault is occurring. Existing approaches to protect an industrial control system, such as failure and diagnostics technologies, may not adequately address these problems - especially when multiple, simultaneous attacks and/faults occur since such multiple faults/failure diagnostic technologies are not designed for detecting stealthy attacks in an automatic manner.

Additionally, existing approaches may use a single classifier model to determine that an anomaly exists. However, a single classifier model cannot meet the requirements for rapid and accurate abnormality detection and localization in complex dynamic environments. It would therefore be desirable to protect a cyber-physical system from cyber-attacks in an automatic and accurate manner even for a complex dynamic environment. <CIT> relates to a system having a plurality of monitoring nodes to generate a series of current monitoring node values over time representing current operation of a cyber-physical system. <CIT> relates to a cyber-physical system may having a plurality of system nodes including a plurality of monitoring nodes each generating a series of current monitoring node values over time that represent current operation of the cyber-physical system.

The present invention relates to a system to protect a cyber physical system according to the appended claims. In some embodiments, a system is provided including a plurality of real-time monitoring nodes to receive streams of monitoring node signal values over time that represent a current operation of the cyber physical system; a local status determination module comprising an ensemble of local agents, the module adapted to determine an anomaly status for one or more nodes; a global status determination module comprising an ensemble of global agents, the module adapted to determine an anomaly status for the cyber physical system; a threat detection computer platform comprising a memory and a computer processor, the threat detection computer platform coupled to the plurality of real-time monitoring nodes and adapted to: receive the monitoring node signal values, generate feature vectors from the received monitoring node signal values; compare via the local status determination module the feature vectors with at least one decision boundary associated with a local abnormal detection model; compare via the global status determination module the feature vectors with at least one decision boundary associated with a global abnormal detection model; and transmit an abnormal alert signal from the local status determination module and the global status determination module based on a result of each comparison.

In some embodiments, a method is provided including providing a local status determination module comprising an ensemble of local agents; providing a global status determination module comprising an ensemble of global agents; receiving a stream of monitoring node signal values from a plurality of real-time monitoring nodes, wherein the monitoring node signal value represent a current operation of the cyber physical system; generating feature vectors from the received monitored node signal values; determining, via the local status determination module, an anomaly status for one or more nodes by comparing the feature vectors with at least one decision boundary associated with a first abnormal detection model; determining, via the global status determination module, an anomaly status for the cyber physical system by comparing the feature vectors with at least one decision boundary associated with a second abnormal detection model; transmitting a first abnormal alert signal and a second abnormal alert signal based on a result of the comparison by the local status determination module and the comparison by the global status determination module.

In some embodiments, a non-transitory computer readable medium storing program code is provided, the program code executable by a computer processor to cause the processor to perform a method to protect a cyber physical system associated with a plurality of monitoring nodes, each generating a series of current monitoring node values over time that represent a current operation of the cyber physical system, the method including: generating feature vectors from the received monitored node signal values; determining, via a local status determination module comprising an ensemble of local agents, an anomaly status for one or more nodes by comparing the feature vectors with at least one decision boundary associated with a first abnormal detection model; determining, via a global status determination module comprising an ensemble of global agents, an anomaly status for the cyber physical system by comparing the feature vectors with at least one decision boundary associated with a second abnormal detection model; transmitting a first abnormal alert signal and a second abnormal alert signal based on a result of the comparison by the local status determination module and the comparison by the global status determination module.

Some technical advantages of some embodiments disclosed herein are improved systems and methods to protect one or more cyber-physical systems from abnormalities, such as cyber-attacks, in an automatic and accurate manner.

With this and other advantages and features that will become hereinafter apparent, a more complete understanding of the nature of the invention can be obtained by referring to the following detailed description and to the drawings appended hereto.

Other embodiments are associated with systems and/or non-transitory computer-readable mediums storing instructions to perform any of the methods described herein.

In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of embodiments. However, it will be understood by those of ordinary skill in the art that the embodiments may be practiced without these specific details. In other instances, well-known methods, procedures, components and circuits have not been described in detail so as not to obscure the embodiments.

Embodiments provide a unified architecture for multi-method multiple model and ensemble approaches for detection and localization/isolation via multi-ensemble decision boundaries and decision fusion mechanisms. As used herein, "detection" may refer to a global anomalous detection for a whole system/industrial asset ("Cyber Physical System" - CPS), while "localization" may refer to local anomalous detection for individual nodes, e.g., sensors, actuators, controller parameters/gains, components, or subsystems. In one or more embodiments a threat detection computer platform may receive time-series measurements from a plurality of the system monitoring nodes (sensors/actuators, control parameters, components, or subsystems). These measurements may more specifically be received by a global determination module and a local determination module. Each of the global determination module and the local determination module may include a plurality of agents (ensemble detectors). The ensemble model may capture the overall behavior of the asset better than any single agent model, specially in the presence of nonlinear or time-varying dynamics. Each of the detectors may execute a different available model based, for example, on different applications. Each of these models may be trained by a different process. For example, in the case of a jet engine, the engine may have different modes of operation - takeoff, cruise and landing. The behavior of the engine during each of these modes may be very different. As such, embodiments provide an agent with a model trained with a given process for the takeoff mode, a model trained with a given process (same or different process than for takeoff) for the cruise mode and a model trained with a given process (same or different process than for either takeoff or cruise) for the landing process.

While a single agent may be used to detect anomalies during each of these modes, for example, it may be better to provide a detector/agent for each individual mode. The reason for this may be that different degrees of data (amount and/or accuracy of data) may be available for each mode, and when a single agent is used, the agent may have to detect anomalies using a model that operates as if all of the modes use a same degree of data - typically the least amount/accuracy. Using different agents allows the agent to use a model best suited to degree of data for that mode, or best suited to other parameters for that mode.

Continuing with the engine example, perhaps for cruise, only measured normal historical data is available having a false positive rate of <NUM>-<NUM>%, while for takeoff and landing both normal historical data as well as simulated data of normal operation and abnormal operation is available having a false positive rate of -<NUM>-<NUM>%. With a single agent, the agent may need to only use normal historical data when detecting anomalies for each of the takeoff, cruise, landing modes of operation.

One or more embodiments, on the other hand, may provide multiple agents, where one agent detects anomalies for the cruise using the normal historical data, while one agent detects anomalies for the takeoff using the normal historical and simulated data, and another agent detects anomalies for the landing using the normal historical and simulated data. A status fusion module may then combine the determined anomaly data from each agent to provide a determined anomaly status for the node or system.

<FIG> is a high-level block diagram of a system <NUM> that may be provided in accordance with some embodiments. The system <NUM> includes monitoring nodes <NUM>, such as sensors, actuators, controller parameters/gains, a component, a subsystem, etc. that generate a series of current monitoring node values <NUM> over time that represent a current operation of a cyber-physical system (e.g., an industrial asset). The current monitoring node values <NUM> may be received by a threat detection computer platform <NUM>, and in particular a data pre-processing element <NUM> on the platform <NUM>. The pre-processing module <NUM> may filter and/or smooth noisy data (e.g., to address gaps in the data, bad data, outliers, etc.). Next the pre-processed ("clean") data may be received by a feature extraction module <NUM>. The feature extraction module <NUM> may generate a feature vector <NUM> for the current monitoring node using the current monitoring node values <NUM>. Note that generation of the feature vector may include processing via one of feature transforms, identity transforms, and feature-based dynamic models. According to some embodiments, at least one of the current monitoring node feature vectors is associated with principal components, statistical features, deep learning features, frequency domain features, time series analysis features, logical features, geographic or position-based locations, and/or interaction features.

The feature vector <NUM> may be received by a global status determination module <NUM> and a local status determination module <NUM>. As described with respect to <FIG>, the global status determination module <NUM> ("detection") and local status determination module <NUM> ("localization") may generate "abnormality" decisions based on feature vectors and global decision boundary(ies) <NUM> and local decision boundaries <NUM>. In particular, the global status determination module <NUM> may generate a global anomaly status <NUM> indicating if the cyber-physical system is experiencing "normal" or "abnormal" operation. According to some embodiments, the global status determination module <NUM> may further generate a global confidence score <NUM> that represents how confident the module is about the anomaly status (risk of anomaly) (e.g., with higher values of the score indicating a greater likelihood of abnormality). The local determination module <NUM> may generate, for each monitoring node, a local anomaly status <NUM> indicating if that monitoring node is experiencing "normal" or "abnormal" operation. The local status determination module <NUM> may generate a local confidence score <NUM> that represents how confident the module is about the anomaly status (risk of anomaly) for each monitoring node (e.g., with higher values of the score indicating a greater likelihood of abnormality).

Note that both determination modules <NUM>, <NUM> may generate a relatively small number of "false positive" decisions (indicating abnormal operation when in fact normal operation is being experienced) and "false negative" decisions (indicating normal operation when in fact abnormal operation is occurring). As a result, the global and local statuses could provide contradictory information. For example, the global status might indicate "normal" operation even when one or more local statuses indicate "abnormal operation. " To address these situations, a decision fusion computer platform <NUM> receives the global status <NUM> and the local status <NUM> (which may itself be a combination of the status of multiple nodes), as well as the global score <NUM> and local score <NUM> and generates a "fused" global status <NUM> and, for each monitoring node, a "fused" local status <NUM>, in addition to a fused confidence score <NUM>/<NUM> for the status <NUM>/<NUM>, respectively. It is noted that the global score <NUM> maybe a single number, while the local score <NUM> may be a vector (multiple numbers) the same size as the local status <NUM>. These statuses are "fused" in the sense that information is merged, and contradictory situations may be avoided.

The system <NUM> may also include a conformance test module <NUM>. The conformance test module <NUM> may receive the local anomaly status <NUM> and local confidence score <NUM> (per node) and may determine (on a node-by-node basis) whether an abnormal local status is "independent" or "dependent" (likely caused by an abnormality existing at another monitoring node) based on a casual dependency matrix, propagation paths, control loops time constraints, etc., as described below with respect to <FIG>. The conformance test module <NUM> may also receive the output from the decision fusion computer platform <NUM> and use this data in the conformance determination (independent or dependent).

The system <NUM> may also include a system/subsystem/node level anomaly forecasting and early warning system <NUM>, which may receive the global anomaly status <NUM>, the global confidence score <NUM> from the global status determination module <NUM>, and output from the decision fusion computer <NUM> as input, and, in turn, output a forecasted status for early warning generation. The early warning may be communicated to the operator as an indication of emerging abnormalities (cyber-threats, faults) for situational awareness, or may be used for early engagement of mitigation or neutralization strategies.

A system/subsystem/node level attack vs. fault separation module <NUM> may also receive the global anomaly status <NUM> and the global confidence score <NUM> from the global status determination module <NUM>, and output from the decision fusion computer platform <NUM> as input, and, in turn, outputs an attack/fault status <NUM> and/or a failure mode <NUM>, as described further below.

The output (local status/confidence score) from the decision fusion computer platform <NUM> and the output (dependent/independent local statuses) of the conformance test module <NUM> may be received by a neutralization module <NUM>. The neutralization module <NUM> may try to cancel the effect of the anomaly by calculating normal values for the affected nodes using values from nodes that have a normal anomaly status. These calculated normal values may be sent to a control system (virtual sensors/back up controller, controller) to neutralize (retum/system curtailment, shut down, etc.) the abnormality.

<FIG> is a high-level architecture of the global status determination module <NUM> shown in <FIG>. The global status determination module <NUM> may include a plurality of agents (an ensemble) <NUM>, each responsible for monitoring a portion of the system. The detection agent <NUM> may be a model that receives the feature vectors <NUM> as input, and outputs an agent anomaly status <NUM>. Each agent <NUM> may receive its own set of features, and may use different sets of monitoring nodes to procure data values. As will be described further below with respect to <FIG>, each model/agent <NUM> may be developed using any of the three methods (<NUM>, <NUM>, <NUM>), such that the determination module <NUM> may include agents developed using different methods from each other. Each agent <NUM> may be trained with data representing a different mode of operation of the CPS, and may run in parallel. As indicated by the dashed lines <NUM>, the different agents <NUM> may share data (e.g., anomaly score, features, etc.) with each other and may use this shared data in generating the anomaly status for that agent. This shared data may create couplings among the agents, which may be leveraged to increase the system accuracy and robustness. The ensemble agents and such couplings may be learned using gradient boosting or ensemble (machine) learning techniques including, but not limited to bootstrap aggregation, Bayesian model combination, Bayesian model averaging, boosting and stacking. The coupling may also be determined in-part using domain-knowledge of the physical behavior of the asset. As described above, each detection agent <NUM> may output at least two items - the agent anomaly status <NUM> (normal/abnormal) and an agent confidence score <NUM> representing the confidence in the status.

The global status determination module <NUM> includes a status fusion module <NUM>. The status fusion module <NUM> may combine all the individual detection agent <NUM> outputs into the final system status (abnormal/normal) <NUM>. The status fusion module <NUM> may use at least one of several different processes to combine the outputs. As a non-exhaustive example, the fusion may be a rule-based fusion or a machine-learning (ML)-based fusion.

With rule-based fusion, when a confidence score is not available, (e.g., when an agent comprises of a simple model such as a K-NN or a decision tree) the status fusion module <NUM> may combine the individual detection agent outputs via one of majority voting or dynamic detection selection (pre-stored look-up table). With the majority voting process, the status is selected based on the majority of statutes reported by the agents. It is noted that when the score is not available, majority voting does not use a weighted average. With dynamic detection agent selection, the status fusion module <NUM> may, for the global status determination module <NUM>, use the nodes and other data, including, but not limited to, mode, ambient condition, etc. to combine the agent outputs to dynamically select the dominant agent in each time instant. The criteria for dynamic detection agent selection are determined using simulation and pre-stored in the system. The dynamic detection agent selection may also be combined with the majority voting to, for example, break a tie.

In a case a confidence score is available, the status fusion module <NUM> may combine the agent outputs by taking a weighted average of the normalized confidence (e.g., risk or probability of anomaly) scores. Each detection agent may have its own set threshold to be compared with its own confidence score to report its own status. In order for the confidence scores from different agents in the ensemble to be comparable to each other, each score is normalized as "distance to its own threshold": <MAT> where scorei and THRi are the confidence score and the threshold of the i-th agent, respectively.

The weights in the weighted average may be obtained from training confusion matrices or from the real-time confidence numbers. The sign of this weighted average determines the overall fused status (negative being "normal" and positive being "abnormal"), and the value of the weighted average represents the overall fused confidence score.

With ML-based fusion, the status fusion module <NUM> may be a classification or regression model trained using any appropriate machine learning technique using a training labeled dataset, including but not limited to linear regression, polynomial models, generalized linear model, extreme learning machine (ELM) regression and deep neural networks. The status fusion module <NUM> may combine the agent outputs based on this trained model.

<FIG> is a high-level architecture of the local status determination module <NUM> shown in <FIG> for multiple localization decisions (e.g., multiple nodes). The module <NUM> may include as many nodes as desired localizations. Each node may have its own localization, and the localizations of the multiple nodes run in parallel. The score and status from each localization (for each node) may be stacked up in a stack <NUM> to form the two vectors: localization confidence scores vector <NUM> and localization statuses vector <NUM>.

<FIG> is high-level architecture for one of the localization decisions (e.g., one node/component/subsystem) <NUM> of the plurality of decisions/nodes of <FIG>. The module <NUM>, may comprise of multiple such ensemble architectures shown in <FIG> per each node, as shown in <FIG>. The local status determination module <NUM> may include a plurality of agents (an ensemble) <NUM>, each responsible for a specific mode or region of operation. The node may represent a sensor, an actuator or a controller gain/parameter, a component or a subsystem. The granularity of localization is determined by the asset physical and operation layout and architecture, as well as system requirements. The localization agent <NUM> may be a model that receives the feature vectors <NUM> as input, and outputs an agent anomaly status <NUM>. Each agent <NUM> may receive its own set of features. As will be described further below with respect to <FIG>, each model/agent <NUM> may be developed using any of the three methods (<NUM>, <NUM>, <NUM>), such that the determination module <NUM> may include agents developed using different methods from each other. Each agent <NUM> may be trained with data representing a different mode of operation of the CPS, and may run in parallel. As indicated by the dashed lines <NUM>, the different agents <NUM> may share data (e.g., anomaly score, features, etc.) with each other and may use this shared data in generating the anomaly status for that agent. This shared data may create couplings among the agents, which may be leveraged to increase the system accuracy and robustness. Such couplings may be learned using gradient boosting or ensemble (machine) learning techniques including, but not limited to Bayesian model combination, Bayesian model averaging, boosting and stacking. The coupling may also be determined in-part using domain-knowledge of the physical behavior of the asset. As described above, each detection agent <NUM> may output at least two items - the agent anomaly status <NUM> (normal/abnormal) and an agent confidence score <NUM> representing the confidence in the status.

Like the global status determination module <NUM>, the local status determination module <NUM> includes the status fusion module <NUM>. The status fusion module <NUM> may combine all the individual detection agent <NUM> outputs into the final node status (abnormal/normal) <NUM>. The status fusion module <NUM> may use at least one of several different processes to combine the outputs. As a non-exhaustive example, the fusion may be a rule-based fusion or a machine-learning (ML)-based fusion.

With rule-based fusion, when a confidence score is not available (e.g., when an agent comprises of a simple model such as a K-NN or a decision tree), the status fusion module <NUM> may combine the individual detection agent outputs via one of majority voting or dynamic detection selection (pre-stored look-up table), as described above.

In a case a confidence score is available, the status fusion module <NUM> may combine the agent outputs by taking a weighted average of the normalized confidence scores, similar to what was described for global detection. The weights may be obtained from training confusion matrices or from the real-time confidence numbers.

<FIG> is a chart depicting three different methods - physics-based <NUM>, data-driven <NUM> and one-class <NUM> - for developing each model/agent <NUM>/<NUM>, such that the determination module <NUM>/<NUM> may include agents developed using different methods from each other.

The physics-based method <NUM> may be a form of two-class supervised learning that uses a physics-based digital twin model which may be used for simulations to generate normal or abnormal training datasets. The normal data from simulation may be combined with normal data from the field to create the normal training dataset <NUM>. The normal training data as well as abnormal synthesized data <NUM> are used to the train the model to establish a decision boundary <NUM>. As the training uses physics-based models, high quality training dataset may be generated both in terms of granularity and coverage, resulting in a very low false positive rate and high accuracies providing excellent predictability and extrapolation.

The data-driven method <NUM> may be a form of two-class supervised learning that uses a data-driven digital twin model <NUM> developed using historical field data via dynamic system modeling methods such as machine learning (e.g., recurrent neural networks) or system identification methods. The data-driven digital twin model may be a complete black box model or a structured grey-box model, e.g., partitioned to represent the control loop structure. Similar to the physics-based digital twin, the data-driven digital twin maybe used to simulate the system and generate synthetic abnormal and normal data. The normal data <NUM> from sensors and actuators (e.g., pressure, flow, speed) and abnormal synthesized data <NUM> is used to train the detection and location models to establish a decision boundary <NUM>. As the training data is obtained using a data-driven digital twin, which may have a lower fidelity than a physic-based model there may be less predictability and extrapolation than with the physics-based method <NUM>. Although, the fidelity of the data-driven digital twin may be increased by increasing the amount and the coverage of the historical field data, and may approach the fidelity of the physics-based model, it is generally expected that a data-driven digital twin has lower fidelity than a physics-based first-principle digital twin. However, this approach is suitable when a physics-based model is not available for the system or a node of the system, and it is expensive and complicated to develop. Note that in any of these approaches, the digital twin (either physics-based or data-driven) is not used as a anomaly detection or localization model, but it is used to generate simulation training data to train those models.

The one-class method <NUM> may be a form of one-class learning that uses only measured normal historical field data <NUM> to establish a decision boundary <NUM>. As the one-class method <NUM> does not use any abnormal data, no asset model or heavy simulations are required to generate training data. This will make the development and deploy process quick and lowers the cost. The performance of detection and location models developed using this approach depends on the quality and availability of the historical field data and the application domain. This approach may be useful when the system or part of the system (e.g. a node) does not have a physic-based model available and is still too complex for developing a well-representative data-driven digital twin.

Each model/agent <NUM>/<NUM> may be trained prior to execution of the system by a method as selected by a designer/development team. The method may be selected based on data availability, as well as other considerations including but not limited to accuracy/complexity/cost. Aside from this, since each method uses a different perspective and different training datasets, and ensemble of agents developed using different methods, may outperform any single agent.

For example, <FIG> is a method that may be provided in accordance with some embodiments. The flow charts described herein do not imply a fixed order to the steps, and embodiments of the present invention may be practiced in any order that is practicable. Note that any of the methods described herein may be performed by hardware, software, or any combination of these approaches. For example, a computer-readable storage medium may store thereon instructions that when executed by a machine result in performance according to any of the embodiments described herein.

At S210, an agent generator <NUM> receives selection of a process to train an agent <NUM>/<NUM>. The selection may be from a developer via selection of a selector on a user interface. The process may be one of the physics-based <NUM>, data-driven <NUM> and one-class <NUM> processes. Then, in S212, the agent generator <NUM> retrieves data for training the agent/model. As described above, when the physics-based <NUM> process is selected, the agent generator may retrieve normal data from simulation and in the field as well as abnormal synthesized data of a physics-based digital twin, the data stored in/ and retrieved from any suitable data store to train the agent/model. In a case the data-driven method <NUM> is selected, the agent generator <NUM> may retrieve measured normal data (sensor and actuator data (e.g., pressure, flow, speed)) and abnormal synthesized data from any suitable data store to train the model/agent. In a case the one-class method is selected, the agent generator <NUM> may retrieve measured normal data from any suitable data store to train the agent/model. Next, in S214, a decision boundary is created based on the retrieved data, as described further below with respect to <FIG>. The agent/model is then trained in S216 using the retrieved data and the established decision boundary.

<FIG> is a high-level architecture of a system <NUM> in accordance with some embodiments. The system <NUM> may include monitoring node sensors <NUM> MN<NUM> through MNN, a "normal space" data source <NUM>, and an "abnormal space" data source <NUM>. The normal space data source <NUM> might store, for each of the plurality of monitoring nodes <NUM>, a series of normal values over time that represent normal operation of a cyber-physical system (e.g., generated by a model or collected from actual sensor data as illustrated by the dashed line in <FIG>). The abnormal space data source <NUM> might store, for each of the monitoring nodes <NUM>, a series of abnormal values that represent abnormal operation of the cyber-physical system (e.g., when the system is experiencing a cyber-attack or a fault).

Information from the normal space data source <NUM> and the abnormal space data source <NUM> may be provided to an abnormal detection model creation computer <NUM> that uses this data to create a decision boundary (that is, a boundary that separates normal behavior from threatened behavior). The decision boundary may then be used by an abnormal detection computer <NUM> executing an abnormal detection model <NUM>. The abnormal detection model <NUM> may, for example, monitor streams of data from the monitoring nodes <NUM> comprising data from sensor nodes, actuator nodes, and/or any other critical monitoring nodes (e.g., sensor nodes MN<NUM> through MNN) and automatically output global and local abnormal alert signal to one or more remote monitoring devices <NUM> when appropriate (e.g., for display to an operator or to have the global and local information fused in accordance with any of the embodiments described herein). As used herein, the term "automatically" may refer to, for example, actions that can be performed with little or no human intervention. According to some embodiments, information about detected threats may be transmitted back to a cyber-physical system control system.

As used herein, devices, including those associated with the system <NUM> and any other device described herein, may exchange information via any communication network which may be one or more of a Local Area Network ("LAN"), a Metropolitan Area Network ("MAN"), a Wide Area Network ("WAN"), a proprietary network, a Public Switched Telephone Network ("PSTN"), a Wireless Application Protocol ("WAP") network, a Bluetooth network, a wireless LAN network, and/or an Internet Protocol ("IP") network such as the Internet, an intranet, or an extranet. Note that any devices described herein may communicate via one or more such communication networks.

The abnormal detection model creation computer <NUM> may store information into and/or retrieve information from various data stores, such as the normal space data source <NUM> and/or the abnormal space data source <NUM>. The various data sources may be locally stored or reside remote from the abnormal detection model creation computer <NUM>. Although a single abnormal detection model creation computer <NUM> is shown in <FIG>, any number of such devices may be included. Moreover, various devices described herein might be combined according to embodiments of the present invention. For example, in some embodiments, the abnormal detection model creation computer <NUM> and one or more data sources <NUM>, <NUM> might comprise a single apparatus. The abnormal detection model creation computer <NUM> functions may be performed by a constellation of networked apparatuses, in a distributed processing or cloud-based architecture.

A user may access the system <NUM> via one of the monitoring devices <NUM> (e.g., a Personal Computer ("PC"), tablet, or smartphone) to select a process to train an agent, view information about and/or manage threat information in accordance with any of the embodiments described herein. In some cases, an interactive graphical display interface may let a user define and/or adjust certain parameters (e.g., abnormal detection trigger levels) and/or provide or receive automatically generated recommendations or results from the abnormal detection model creation computer <NUM> and/or abnormal detection computer <NUM>.

some embodiments described herein may use time series data from one or more monitoring nodes <NUM> from a physical (i.e., industrial or enterprise) asset and provide a reliable abnormality detection with low false positive rate. The system may extract features from the time series data for each monitoring node. The term "feature" may refer to, for example, mathematical characterizations of data. Examples of features as applied to data might include the maximum and minimum, mean, standard deviation, variance, settling time, Fast Fourier Transform ("FFT") spectral components, linear and non-linear principal components, independent components, sparse coding, deep learning, etc. The type and number of features for each monitoring node might be optimized using domain-knowledge and/or a feature discovery process. The features may be, for example, calculated over a sliding window with consecutive samples of specified duration from time series data The length of the window and the duration of overlap for each batch may be determined from domain knowledge and an inspection of the data or using batch processing. Note that features may be computed at the local level (associated with each monitoring node) and the global level (associated with all the monitoring nodes, i.e., the whole asset). The time-domain values of the nodes or their extracted features may be, according to some embodiments, normalized for better numerical conditioning.

<FIG> illustrates a model creation method that might be performed by some or all of the elements of the system <NUM> described with respect to <FIG>. At S410, the system may retrieve, for each of a plurality of monitoring nodes (e.g., sensor nodes, actuator nodes, controller nodes, etc.), a series of normal values over time that represent normal operation of the Cyber-Physical System ("CPS") and a set of normal feature vectors may be generated. Similarly, at S420 the system may retrieve, for each of the plurality of monitoring nodes, a series of abnormal (e.g., attacked) values over time that represent an abnormal operation of the cyber-physical system and a set of abnormal feature vectors may be generated. The series of normal and/or abnormal values might be obtained, for example, by running Design of Experiments ("DoE") on a cyber-physical system. At S430, a decision boundary may be automatically calculated for an abnormal detection model based on the set of normal feature vectors and the set of abnormal feature vectors. According to some embodiments, the decision boundary might be associated with a line, a hyperplane, a non-linear boundary separating normal space from threatened space, and/or a plurality of decision boundaries. Moreover, a decision boundary might comprise a multi-class decision boundary separating normal space, attacked space, and degraded operation space (e.g., when a sensor fault occurs). In addition, note that the abnormal detection model might be associated with the decision boundary, feature mapping functions, and/or feature parameters.

The decision boundary can then be used to detect abnormal operation (e.g., as might occur during cyber-attacks). For example, <FIG> is an abnormal alert method according to some embodiments. At S510, the system may receive, from a plurality of monitoring nodes, a series of current values over time that represent a current operation of the cyber-physical system. At S520, an attack detection platform computer may then generate, based on the received series of current values, a set of current feature vectors. At S530, an abnormal detection model may be executed to transmit an abnormal alert signal based on the set of current feature vectors and a decision boundary when appropriate (e.g., when a cyber-attack is detected). According to some embodiments, one or more response actions may be performed when an abnormal alert signal is transmitted. For example, the system might automatically shut down all or a portion of the cyber-physical system (e.g., to let the detected potential cyber-attack be further investigated). As other examples, one or more parameters might be automatically modified, a software application might be automatically triggered to capture data and/or isolate possible causes, etc..

Some embodiments described herein may take advantage of the physics of a control system by learning a priori from tuned high-fidelity equipment models and/or actual "on the job" data to detect single or multiple simultaneous adversarial threats to the system. Moreover, according to some embodiments, all monitoring node data may be converted to features using advanced feature-based methods, and the real-time operation of the control system may be monitored in substantially real-time. Abnormalities may be detected by classifying the monitored data as being "normal" or disrupted (or degraded). This decision boundary may be constructed using dynamic models and may help enable early detection of vulnerabilities (and potentially avert catastrophic failures) allowing an operator to restore the control system to normal operation in a timely fashion.

Note that an appropriate set of multi-dimensional feature vectors, which may be extracted automatically (e.g., via an algorithm) and/or be manually input, might comprise a good predictor of measured data in a low dimensional vector space. According to some embodiments, appropriate decision boundaries may be constructed in a multi-dimensional space using a data set which is obtained via scientific principles associated with DoE techniques. Moreover, multiple algorithmic methods (e.g., support vector machines or machine learning techniques) may be used to generate decision boundaries. Since boundaries may be driven by measured data (or data generated from high-fidelity models), defined boundary margins may help to create an abnormal zone in a multi-dimensional feature space. Moreover, the margins may be dynamic in nature and adapted based on a transient or steady state model of the equipment and/or be obtained while operating the system as in self-learning systems from incoming data stream. According to some embodiments, a training method may be used for supervised learning to teach decision boundaries. This type of supervised learning may take into account on operator's knowledge about system operation (e.g., the differences between normal and abnormal operation).

<FIG> illustrates an off-line boundary creation process <NUM> in accordance with some embodiments. Information about threats, spoofing, attack vectors, vulnerabilities, etc. <NUM> may be provided to models <NUM> and/or a training and evaluation database <NUM> created using DoE techniques. The models <NUM> may, for example, simulate data <NUM> from monitoring nodes to be used to compute features that are assembled into a feature vector <NUM> to be stored in the training and evaluation database <NUM>. The data in the training and evaluation database <NUM> may then be used to compute decision boundaries <NUM> to distinguish between normal operation and abnormal operation. According to some embodiments, the process <NUM> may include a prioritization of monitoring nodes and anticipated attack vectors to form one or more data sets to develop decision boundaries. Attack vectors are abnormal values at critical inputs where malicious attacks can be created at the domain level that will make the system go into threatened/abnormal space. In addition, the models <NUM> may comprise high-fidelity models that can be used to create a data set (e.g., a set that describes threat space as "levels of threat conditions in the system versus quantities from the monitoring nodes"). The data <NUM> from the monitoring nodes might be, for example, quantities that are captured for a length of from <NUM> to <NUM> seconds from sensor nodes, actuator nodes, and/or controller nodes (and a similar data set may be obtained for "levels of normal operating conditions in the system versus quantities from the monitoring nodes"). This process will result in data sets for "abnormal space" and "normal space. " The <NUM> to <NUM> seconds long quantities may be used to compute features <NUM> using feature engineering to create feature vectors. These feature vectors can then be used to obtain a decision boundary that separates the data sets for abnormal space and normal space (used to detect an anomaly such as a cyber-attack).

Since attacks might be multi-prong (e.g., multiple attacks might happen at once), DoE experiments may be designed to capture the attack space (e.g., using full factorial, Taguchi screening, central composite, and/or Box-Behnken). When models are not available, these DoE methods can also be used to collect data from real-world asset control system. Experiments may run, for example, using different combinations of simultaneous attacks. Similar experiments may be run to create a data set for the normal operating space. According to some embodiments, the system may detect "degraded" or faulty operation as opposed to a threat or attack. Such decisions may require the use of a data set for a degraded and/or faulty operating space.

<FIG> illustrates a real-time process to protect a cyber-physical system according to some embodiments. At S710, current data from monitoring nodes may be gathered (e.g., in batches of from <NUM> to <NUM> seconds). At S720, the system may compute features and form feature vectors. For example, the system might use weights from a principal component analysis as features. At S730, an abnormal detection engine may compare location of feature vectors to a decision boundary to make a determination (and output an abnormal signal if necessary). According to some embodiments, monitoring node data from models (or from real systems) may be expressed in terms of features since features are a high-level representation of domain knowledge and can be intuitively explained. Moreover, embodiments may handle multiple features represented as vectors and interactions between multiple sensed quantities might be expressed in terms of "interaction features.

Note that many different types of features may be utilized in accordance with any of the embodiments described herein, including principal components (weights constructed with natural basis sets) and statistical features (e.g., mean, variance, skewness, kurtosis, maximum, minimum values of time series signals, location of maximum and minimum values, independent components, etc.). Other examples include deep learning features (e.g., generated by mining experimental and/or historical data sets) and frequency domain features (e.g., associated with coefficients of Fourier or wavelet transforms). Embodiments may also be associated with time series analysis features, such as cross-correlations, auto-correlations, orders of the autoregressive, moving average model, parameters of the model, derivatives and integrals of signals, rise time, settling time, neural networks, etc. Still other examples include logical features (with semantic abstractions such as "yes" and "no"), geographic/position locations, and interaction features (mathematical combinations of signals from multiple monitoring nodes and specific locations). Embodiments may incorporate any number of features, with more features allowing the approach to become more accurate as the system learns more about the physical process and threat. According to some embodiments, dissimilar values from monitoring nodes may be normalized to unit-less space, which may allow for a simple way to compare outputs and strength of outputs.

<FIG> is an example <NUM> associated with a cyber-physical system in accordance with some embodiments. In particular, the example includes a controller and actuator portion <NUM> subject to actuator and controller attacks, a gas turbine portion <NUM> subject to state attacks, and sensors <NUM> subject to sensor attacks. By way of examples only, the sensors <NUM> might comprise physical and/or virtual sensors associated with temperatures, airflows, power levels, etc. The actuators might be associated with, for example, motors. By monitoring the information in the cyber-physical system, a threat detection platform may be able to detect cyber-attacks (e.g., using feature vectors and a decision boundary) that could potentially cause a large amount of damage.

<FIG> illustrates <NUM> three dimensions of monitoring node outputs in accordance with some embodiments. In particular, a graph <NUM> plots monitoring node outputs ("+") in three dimensions, such as dimensions associated with Principal Component Features ("PCF"): w1, w2, and w3. Moreover, the graph <NUM> includes an indication of a normal operating space decision boundary <NUM>. Although a single contiguous boundary <NUM> is illustrated in <FIG>, embodiments might be associated with multiple regions. Note that PCF information may be represented as weights in reduced dimensions. For example, data from each monitoring node may be converted to low dimensional features (e.g., weights). According to some embodiments, monitoring node data is normalized as follows: <MAT> where S stands for a monitoring node quantity at "k" instant of time. Moreover, output may then be expressed as a weighted linear combination of basis functions as follows: <MAT> where S<NUM> is the average monitoring node output with all threats, wj is the jth weight, and Ψj is the jth basis vector. According to some embodiments, natural basis vectors are obtained using a covariance of the monitoring nodes' data matrix. Once the basis vectors are known, weight may be found using the following equation (assuming that the basis sets are orthogonal): <MAT> Note that weights may be an example of features used in a feature vector.

Thus, embodiments may enable the passive detection of indications of multi-class abnormal operations using real-time signals from monitoring nodes. Moreover, the detection framework may allow for the development of tools that facilitate proliferation of the invention to various systems (e.g., turbines) in multiple geolocations. According to some embodiments, distributed detection systems enabled by this technology (across multiple types of equipment and systems) will allow for the collection of coordinated data to help detect multi-prong attacks. Note that the feature-based approaches described herein may allow for extended feature vectors and/or incorporate new features into existing vectors as new learnings and alternate sources of data become available. As a result, embodiments may detect a relatively wide range of cyber-threats (e.g., stealth, replay, covert, injection attacks, etc.) as the systems learn more about their characteristics. Embodiments may also reduce false positive rates as systems incorporate useful key new features and remove ones that are redundant or less important. Note that the detection systems described herein may provide early warning to cyber-physical system operators so that an attack may be thwarted (or the effects of the attack may be blunted), reducing damage to equipment.

According to some embodiments, a system may further localize an origin of a threat to a particular monitoring node. For example, the localizing may be performed in accordance with a time at which a decision boundary associated with one monitoring node was crossed as compared to a time at which a decision boundary associated with another monitoring node was crossed. According to some embodiments, an indication of the particular monitoring node might be included in a threat alert signal.

Some embodiments of the algorithm may utilize feature-based learning techniques based on high-fidelity physics models and/or machine operation data (which would allow the algorithm to be deployed on any system) to establish a high dimensional decision boundary. As a result, detection may occur with more precision using multiple signals, making the detection more accurate with less false positives. Moreover, embodiments may detect multiple attacks on control signals, and rationalize where the root cause attack originated. For example, the algorithm may decide if a signal is anomalous because of a previous signal attack, or if it is instead independently under attack. This may be accomplished, for example, by monitoring the evolution of the features as well as by accounting for time delays between attacks.

A cyber-attack detection and localization algorithm may process a real-time cyber-physical system signal data stream and then compute features (multiple identifiers) which can then be compared to the signal-specific decision boundary. A block diagram of a system <NUM> utilizing a signal-specific cyber-physical system abnormality detection and localization algorithm according to some embodiments is provided in <FIG>. In particular, a gas turbine <NUM> provides information to sensors <NUM> which helps controllers with electronics and processors <NUM> adjust actuators <NUM>. A threat detection system <NUM> may include one or more high-fidelity physics-based digital-twin models/data-driven digital twin models/historical field data <NUM> associated with the turbine <NUM> to create normal data <NUM> and/or abnormal data <NUM>. The normal data <NUM> and abnormal data <NUM> may be accessed by a feature discovery component <NUM> and processed by decision boundary algorithms <NUM> while off-line (e.g., not necessarily while the gas turbine <NUM> is operating). The decision boundary algorithms <NUM> may generate an abnormal model including decision boundaries for various monitoring nodes. Each decision boundary may separate two data sets in a high dimensional space which is constructed by running a binary classification algorithm, such as a support vector machine using the normal data <NUM> and abnormal data <NUM> for each monitoring node signal (e.g., from the sensors <NUM>, controllers <NUM>, and/or the actuators <NUM>).

A real-time threat detection platform <NUM> may receive the boundaries along with streams of data from the monitoring nodes. The platform <NUM> may include a feature extraction on each monitoring node element <NUM> and a normalcy decision <NUM> with an algorithm to detect attacks in individual signals using signal specific decision boundaries, as well rationalize attacks on multiple signals, to declare which signals were attacked (or are otherwise abnormal), and which became anomalous due to a previous attack on the system via a localization module <NUM>. An accommodation element <NUM> may generate outputs <NUM>, such as an anomaly decision indication (e.g., an abnormal) alert signal), a controller action, and/or a list of abnormal monitoring nodes.

During real-time detection, contiguous batches of control signal data may be processed by the platform <NUM>, normalized and the feature vector extracted. The location of the vector for each signal in high-dimensional feature space may then be compared to a corresponding decision boundary. If it falls within the abnormal region, then a cyber-attack may be declared. The algorithm may then make a decision about where the attack originally occurred. An attack may sometimes be on the actuators <NUM> and then manifested in the sensor <NUM> data. Attack assessments might be performed in a post decision module (e.g., the localization element <NUM>) to isolate whether the attack is related to the sensor, controller, or actuator (e.g., indicating which part of the monitoring node). This may be done by individually monitoring, overtime, the location of the feature vector with respect to the hard decision boundary. For example, when a sensor <NUM> is spoofed, the attacked sensor feature vector will cross the hard decision boundary earlier than the rest of the vectors as described with respect to <FIG>. If a sensor <NUM> is declared to be anomalous, and a command to the auxiliary equipment is later determined to be anomalous, it may be determined that the original attack, such as signal spoofing, occurred on the sensor <NUM>. Conversely, if the signal to the auxiliary equipment was determined to be anomalous first, and then later manifested in the sensor <NUM> feedback signal, it may be determined that the signal to the equipment was initially attacked.

According to some embodiments, it may be detected whether or not a signal is in the normal operating space (or abnormal space) through the use of localized decision boundaries and real-time computation of the specific signal features. Moreover, an algorithm may differentiate between a sensor being attacked as compared to a signal to auxiliary equipment being attacked. The control intermediary parameters and control logical(s) may also be analyzed using similar methods. Note that an algorithm may rationalize signals that become anomalous. An attack on a signal may then be identified.

<FIG> illustrates <NUM> boundaries and feature vectors for various monitoring node parameters in accordance with some embodiments. In particular, for each parameter a graph includes a first axis representing value weight <NUM> ("w1"), a feature <NUM>, and a second axis representing value weight <NUM> ("w2"), a feature <NUM>. Values for w1 and w2 might be associated with, for example, outputs from a Principal Component Analysis ("PCA") that is performed on the input data. PCA might be one of the features that might be used by the algorithm to characterize the data, but note that other features could be leveraged.

A graph is provided for compressor discharge temperature <NUM>, compressor pressure ratio <NUM>, compressor inlet temperature <NUM>, fuel flow <NUM>, generator power <NUM>, and gas turbine exhaust temperature <NUM>. Each graph includes a hard boundary <NUM> (solid curve), inner boundary <NUM> (dotted curve), and outer boundary <NUM> (dashed curve) and an indication associated with current feature location for each monitoring node parameter (illustrated with an "X" on the graphs). As illustrated in <FIG>, the current monitoring node location is between the minimum and maximum boundaries (that is, the "X" is between the dotted and dashed lines). As a result, the system may determine that the operation of the cyber-physical system control system is normal (and no threat is being detected indicating that the system is currently under attack or that a naturally occurring fault has occurred).

<FIG> illustrates <NUM> subsequent boundaries and feature vectors for these parameters. Consider, for example, a feature vector movement <NUM> for the compressor discharge pressure. Even though feature vector <NUM> has moved, it is still within the maximum and minimum boundaries and, as a result, normal operation of that monitoring node may be determined. This is the case for the first five graphs in <FIG>. In this example, a feature vector movement <NUM> for the gas turbine exhaust temperature has exceeded with maximum boundary and, as a result, abnormal operation of that monitoring node may be determined. For example, a threat may exist for the exhaust temperature scale factor, which is a corrective value. The result is that the feature for the intermediary monitoring node signal feature vector illustrated in <FIG> moves <NUM> such that it is anomalous. The algorithm detects this cyber-attack, and two parallel actions might be initiated. One action may be post processing of the signal to discover what was attacked, in this case if the system has been monitoring each exhaust thermocouple, it may conclude that none of them are currently abnormal. Therefore, it may be determined that something used to calculate this feature was attacked. The other action may be to continually monitor and detect additional attacks. Such an approach may facilitate a detection of multiple signal attacks.

Given the example of <FIG>, assume that the gas turbine exhaust temperature signal was attacked. This may cause the system to respond in such a way so as to put other signals into an abnormal state. This is illustrated <NUM> in <FIG>, where the attack has already been detected and now other signals shown to be abnormal. In particular, feature movement for the compressor discharge pressure <NUM>, compressor pressure ratio <NUM>, compressor inlet temperature <NUM>, and fuel flow <NUM> have all become abnormal (joining the feature vector for the gas turbine exhaust temperature <NUM>). Note that the feature vector for generator power did not become abnormal. In order to decide whether or not these signals <NUM>, <NUM>, <NUM>, <NUM> are truly currently under attack, a historical batch with pertinent feature vector information may be kept for some duration of time. Then when an attack is detected on another signal, this batch is examined, and the time at which the confirmed attack on gas turbine exhaust temperature as well as several subsequent elements is analyzed.

Note that one signal rationalization might be associated with a system time delay. That is, after a sensor is attacked there might be a period of time before the system returns to a steady state. After this delay, any signal that becomes anomalous might be due to an attack as opposed to the system responding.

The current methods for detecting abnormal conditions in monitoring nodes are limited to Fault Detection Isolation and Accommodation ("FDIA"), which itself is very limited. The cyber-attack detection and localization algorithms described herein can not only detect abnormal signals of sensors, but can also detect signals sent to auxiliary equipment, control intermediary parameters and/or control logical(s). The algorithm can also understand multiple signal attacks. One challenge with correctly identifying a cyber-attack threat is that it may occur with multiple sensors being impacted by malware. According to some embodiments, an algorithm may identify in real-time that an attack has occurred, which sensor(s) are impacted, and declare a fault response. To achieve such a result, the detailed physical response of the system must be known to create acceptable decision boundaries. This might be accomplished, for example, by constructing data sets for normal and abnormal regions by running DoE experiments on high-fidelity models. A data set for each sensor might comprise a feature vector for given threat values (e.g., temperature, airflow, etc.). Full factorial, Taguchi screening, central composite and Box-Behnken are some of the known design methodologies used to create the attack space. When models are not available, these DoE methods are also used to collect data from real-world cyber-physical systems. Experiments may be run at different combinations of simultaneous attacks. In some embodiments, the system may detect degraded/faulty operation as opposed to a cyber-attack. Such decisions might utilize a data set associated with a degraded/faulty operating space. At the end of this process, the system may create data sets such as "attack v/s normal" and "degraded v/s normal" for use while constructing decision boundaries. Further note that a decision boundary may be created for each signal using data sets in feature space. Various classification methods may be used to compute decision boundaries. For example, binary linear and non-linear supervised classifiers are examples of methods that could be used to obtain a decision boundary.

Note that multiple vector properties might be examined, and the information described with respect to <FIG> may be processed to determine if the signal had been trending in a specific direction as the attack was detected (or if it had just been moving due to noise). Had the signal been uniformly trending as the attack took place and afterward, then this signal is a response to the original attack and not an independent attack.

According to some embodiments, the system may localize or otherwise analyze an origin of the threat to a particular monitoring node. For example, the localizing may be performed in accordance with a time at which a decision boundary associated with one monitoring node was crossed as compared to a time at which a decision boundary associated with another monitoring node was crossed. According to some embodiments, an indication of the particular monitoring node might be included in the threat alert signal.

Some embodiments described herein may take advantage of the physics of a cyber-physical system by learning a priori from tuned high-fidelity equipment models and/or actual "on the job" data to detect single or multiple simultaneous adversarial threats to the system. Moreover, according to some embodiments, all monitoring node data may be converted to features using advanced feature-based methods, and the real-time operation of the cyber-physical system may be monitored in substantially real-time. Abnormalities may be detected by classifying the monitored data as being "normal" or disrupted (or degraded). This decision boundary may be constructed using dynamic models and may help to enable early detection of vulnerabilities (and potentially avert catastrophic failures) allowing an operator to restore the cyber-physical system to normal operation in a timely fashion.

Thus, some embodiments may provide an advanced anomaly detection algorithm to detect cyber-attacks on, for example, key cyber-physical system control sensors. The algorithm may identify which signals(s) are being attacked using control signal-specific decision boundaries and may inform a cyber-physical system to take accommodative actions. In particular, a detection and localization algorithm might detect whether a sensor, auxiliary equipment input signal, control intermediary parameter, or control logical are in a normal or anomalous state. Some examples of cyber-physical system monitoring nodes that might be analyzed include: critical control sensors; control system intermediary parameters; auxiliary equipment input signals; and/or logical commands to controller.

A cyber-attack detection and localization algorithm may process a real-time cyber-physical system signal data stream and then compute features (multiple identifiers) which can then be compared to the sensor specific decision boundary. In some embodiments, generating features may involve simply performing an identity transform. That is, the original signal might be used as it is.

Feature vectors may be generated on a monitoring-node-by-monitoring node basis and may be considered "local" to each particular monitoring node. <FIG> is an example of a "global" abnormality protection system <NUM> in accordance with some embodiments when multiple gas turbines are involved in a system. In particular, the global system <NUM> includes three turbines (A, B, and C) and batches of values <NUM> from monitoring nodes are collected for each generated over a period of time (e.g., <NUM> to <NUM> seconds). The values for each node may be "local" to a given node. According to some embodiments, the batches of values <NUM> from monitoring nodes overlap in time. The values <NUM> from monitoring nodes may, for example, be stored in a matrix <NUM> arranged by time (t<NUM>, t<NUM>, etc.) and by type of monitoring node (Si, S<NUM>, etc.). Feature engineering components <NUM> may use information in each matrix <NUM> to create a feature vector <NUM> (local feature vector) for each of the three turbines (e.g., the feature vector <NUM> for turbine C might include FSC1, FSC2, etc.). The three local feature vectors <NUM> may then be combined into a single global feature vector <NUM> for the system <NUM>. Interaction features <NUM> may be applied (e.g., associated with A*B*C, A+B+C, etc.) and an anomaly detection engine <NUM> may compare the result with a decision boundary and output a global abnormal alert signal when appropriate.

Thus, a system may generate both local normal/abnormal decisions (for each monitoring node) and a global normal/abnormal decision (for the entire cyber-physical system). Note, however, that in both cases false positive and false negative decisions may occur. As a result, the local decisions and global decisions might provide contradictory information (e.g., a local monitoring node might be declared abnormal while the entire system is declared normal). To address this situation, some embodiments described herein "fuse" the local and global decisions in a consistent fashion. For example, <FIG> is a method <NUM> for detection and localization decision fusion according to some embodiments. At S <NUM> a global status is determined for a cyber-physical system (along with a global certainty score indicating how likely it is that the that system is "abnormal") and at S1520 local statuses are determined (along with local certainty scores on a none-by-node basis). If both a local status is "normal" at S1522 and the global status is normal at S1530, the node status remains normal at S1540 (that is, there is no conflicting information). If the global status is "normal" (e.g., associated with a negative anomaly score) while a local status of any of the nodes is "abnormal" at S1522, there is a conflict. As a result, the system compares a global certainty score to a pre-determined tuning parameter α at S1512. If the global certainty score is greater than α at S1512, the global status is changed to "abnormal" at S1514 (and that node remains "abnormal" at S1550) - otherwise, the global status and local status remain "normal" at S1540. The tuning parameter α may act as a threshold on the proximity of the global features to the global decision boundary.

If the global status is "abnormal" and a particular node's status is "normal" at S1530, it is determined if any other node in the system is "abnormal" at S1560. If so, there is no conflict (that is, the other node with an "abnormal" status is causing the global abnormality) and neither status needs to be changed. If the global status is "abnormal" and all local statuses (for all monitoring nodes) are "normal" there is a conflict. In this case, the node with the maximum local certainty score at S1570 is declared to be "abnormal" at S1550. The local scores may be normalized to become comparable. In this way, embodiments described herein may provide systems and methods to fuse the outcomes of abnormality detection and localization modules in order to make them coordinated and consistent.

As described above, embodiments may increase efficiency and accuracy by having abnormality detection and localization modules run in parallel. Since each of those methods provide an independent decision, which in practice may not be always correct (due false alarms and false negatives of each), embodiments described herein may fuse the decisions and make them consistent at all times. Note that the parallel execution of detection and localization modules may make them faster, more accurate, and enable parallel computing for computational efficiency. It may also provide two independent sources of decision. The decision fusion makes the two modules consistent and the overall decision more accurate as compared to each individual one.

According to some embodiments, the local certainty scores may be normalized (e.g., into a probability) to be comparable with each other. For example, the normalization might be performed via any appropriate smooth activation function, sigmoid function (a mathematical function having a characteristic S-shaped curve), hyperbolic tangent function (tanh), etc..

<FIG> is a detection and localization system architecture <NUM> according to some embodiments. After feature extraction <NUM> is performed on monitoring node data, the result is provided to global detection <NUM> and local detection <NUM>. The global detection <NUM> uses the features and a global decision boundary to determine an initial global status. The local detection <NUM> uses dynamic models, local boundaries, and a multi variable normal distribution table to determine initial local statuses on a node-by-node basis. The decisions from the global detection <NUM> and local detection <NUM> undergo decision fusion <NUM> to generate fused global and local statuses.

According to some embodiments, a conformance test <NUM> may further determine (on a node-by-node basis) whether an abnormal local status is "independent" or "dependent" (likely caused by an abnormality existing at another monitoring node) based on a casual dependency matrix, propagation paths, control loops time constraints, etc. For example, <FIG> is a method of determining whether an attack is an independent attack or dependent attack in accordance with some embodiments. According to some embodiments, three tests may be performed to determine if an attack should be classified as an "independent attack" or a "dependent attack:" (<NUM>) a causal dependency test, (<NUM>) a propagation path test, and (<NUM>) a time separation test. Together, these three tests may be referred to herein as an "attack dependency conformance test. " At S1710, a causal dependency matrix may be used to determine if the current attack was potentially caused by a previous attack. If the current attack could not have been caused by a previous attack at S1710, it is classified as an "independent attack" at S1720. In this causality test, the system may check whether there is a potential causal dependency between the newly detected attack and any previously detected attack on other monitoring nodes. This check might be based on, for example, a binary matrix of causal dependencies between any two nodes. The causal dependency matrix might be generated, according to some embodiments, based on domain knowledge. If no such possible dependencies exist the attack is reported as an "independent attack" at S1720. Otherwise, the system may perform a second check.

At S1730 a propagation paths map may be used to determine if the current attack potentially propagated from a previous attack. If the current attack could not have propagated from a previous attack at S1730, it is classified as an "independent attack" at S1720. In this propagation test, for each causal dependency the system may check whether a propagation path is fulfilled. This might mean that, for example, if the effect of node <NUM> being under attack is propagated to node <NUM>, through node <NUM>, then an anomaly in node <NUM> can cause an anomaly on node <NUM> only if node <NUM> is already anomalous. The anomaly propagation paths might also be defined by domain knowledge and pre-stored in the localization system. If no such propagation paths are fulfilled, then the attack is reported an "independent attack" at S1720. Otherwise, the system may perform the third check.

At S1740, control loops time constraints may be used to determine if the current attack was potentially caused by a previous attack based on time separation. If the current attack could not have been caused by a previous attack based on time separation at S1740, it is classified as an "independent attack" at S1720. This time separation test may utilize the fact that if the attacked monitoring under investigation is an artifact of the closed-loop feedback system, then the effect should arise within a time window between the rise time and the settling time of the control loop corresponding to the monitoring node. However, since the system uses a dynamic estimator, a propagation time may need to be added throughout the estimator. Using n features, and p lags in the models, the dynamic estimator will have n * p states, and therefore adds n * p sampling times delay into the system. Therefore, the expected time window for a dependent attack to occur might be defined by: <MAT> where Δt is the time after any previously detected attacks on other nodes that has passed checks <NUM> and <NUM>, and τ is the time constant of the control loop responsible for the current node under investigation. If such a time-separation check is not passed, the system reports the attack as an independent attack at S1720. In order the get accurate time instances of the status change events, the module <NUM> may also receive pre-fused node status and scores from <NUM>.

If it is determined at S1750 that the current attack meets the time separation test (and, therefore, also meets both the propagation test of S1730 and the causal dependency test of S1720), the current attack is classified as a "dependent attack" at S1750.

Note that other attack and anomaly detection techniques may only provide a binary status of the overall system (whether it is under attack or not). Embodiments described herein may also provide an additional layer of information by localizing the attack and determining not only if the system is under attack (or not) but also which node is exactly under attack. Note that attack localization information may be important when responding to the attack, including operator action plans and resilient control under attack. Embodiments described herein may handle multiple simultaneous anomalies in the system, which is beyond the capability of the conventional fault detection systems. This may also let the approaches described herein be used as a fault detection and isolation technique for more sophisticated, multiple-fault scenarios. Further, distributed detection and localization systems enabled by embodiments described herein across multiple equipment and systems may allow for a coordination of data to detect and precisely pin-point coordinated multi-prong attacks. This may further enable a relatively quick way to perform forensics and/or analysis after an attack.

Note that some embodiments may analyze information in the feature space, which has many advantages over working in the original signal spaces, including high-level data abstraction and modeling high dimensional spaces without adding substantial computational complexity. The feature-based method for localization may also extend feature vectors and/or incorporate new features into existing vectors as new learnings or alternate sources of data become available. Embodiments described herein may also enable use of heterogeneous sensor data in a large scale interconnected system, even when the data comes from many geospatially located heterogeneous sensors (i.e., conventional plant sensors, unconventional sensors such as cell-phone data, logical, etc.). This may offer additional commercial advantages for post-mortem analysis after an attack.

Note that the embodiments described herein may be implemented using any number of different hardware configurations. For example, <FIG> is a block diagram of a cyber-physical system protection platform <NUM> that may be, for example, associated with the systems <NUM>, <NUM> of <FIG> and <FIG>, respectively, and/or any other system described herein. The cyber-physical system protection platform <NUM> comprises a processor <NUM>, such as one or more commercially available Central Processing Units ("CPUs") in the form of one-chip microprocessors, coupled to a communication device <NUM> configured to communicate via a communication network (not shown in <FIG>). The communication device <NUM> may be used to communicate, for example, with one or more remote monitoring nodes, user platforms, digital twins, etc. The cyber-physical system protection platform <NUM> further includes an input device <NUM> (e.g., a computer mouse and/or keyboard to input cyber-physical system parameters and/or modeling information) and/an output device <NUM> (e.g., a computer monitor to render a display, provide alerts, transmit recommendations, and/or create reports). According to some embodiments, a mobile device, monitoring physical system, and/or PC may be used to exchange information with the cyber-physical system protection platform <NUM>.

The processor <NUM> also communicates with a storage device <NUM>. The storage device <NUM> may comprise any appropriate information storage device, including combinations of magnetic storage devices (e.g., a hard disk drive), optical storage devices, mobile telephones, and/or semiconductor memory devices. The storage device <NUM> stores a program <NUM> and/or cyber-physical system protection engine <NUM> for controlling the processor <NUM>. The processor <NUM> performs instructions of the programs <NUM>, <NUM>, and thereby operates in accordance with any of the embodiments described herein. For example, the processor <NUM> may receive a selection of method to train an agent, may train the agent per the selected method, and then execute the agent to determine whether a monitoring node, or the cyber-physical system has a status of "normal" or "abnormal". The processor <NUM> may then output the determined status to a user interface or other system.

The programs <NUM>, <NUM> may be stored in a compressed, uncompiled and/or encrypted format. The programs <NUM>, <NUM> may furthermore include other program elements, such as an operating system, clipboard application, a database management system, and/or device drivers used by the processor <NUM> to interface with peripheral devices.

As used herein, information may be "received" by or "transmitted" to, for example: (i) the cyber-physical system protection platform <NUM> from another device; or (ii) a software application or module within the cyber-physical system protection platform <NUM> from another software application, module, or any other source.

The following illustrates various additional embodiments of the invention. These do not constitute a definition of all possible embodiments, and those skilled in the art will understand that the present invention is applicable to many other embodiments. Further, although the following embodiments are briefly described for clarity, those skilled in the art will understand how to make any changes, if necessary, to the above-described apparatus and methods to accommodate these and other embodiments and applications.

Although specific hardware and data configurations have been described herein, note that any number of other configurations may be provided in accordance with embodiments of the present invention (e.g., some of the information associated with the databases described herein may be combined or stored in external systems). Moreover, although some embodiments are focused on gas turbines, any of the embodiments described herein could be applied to other types of cyber-physical systems including power grids, dams, locomotives, airplanes, and autonomous vehicles (including automobiles, trucks, drones, submarines, etc.).

Claim 1:
A system to protect a cyber physical system, comprising:
a plurality of real-time monitoring nodes to receive streams of monitoring node signal values over time that represent a current operation of the cyber physical system;
a local status determination module (<NUM>) comprising an ensemble of local agents, the module adapted to determine an agent anomaly status (<NUM>) for one or more nodes;
a global status determination module (<NUM>) comprising an ensemble of global agents, the module adapted to determine an agent anomaly status (<NUM>) for the cyber physical system;
a threat detection computer platform (<NUM>) comprising a memory and a computer processor, the threat detection computer platform coupled to the plurality of real-time monitoring nodes and adapted to:
receive the monitoring node signal values,
generate feature vectors (<NUM>) from the received monitoring node signal values;
compare via the local status determination module (<NUM>) the feature vectors (<NUM>) with at least one decision boundary (<NUM>) associated with a local abnormal detection model;
compare via the global status determination module (<NUM>) the feature vectors (<NUM>) with at least one decision boundary (<NUM>) associated with a global abnormal detection model; and
transmit an abnormal alert signal from the local status determination module (<NUM>) and the global status determination module (<NUM>) based on a result of each comparison, wherein each local agent is adapted to determine an agent anomaly status (<NUM>) for a given node and a confidence score (<NUM>/<NUM>) for the agent anomaly status (<NUM>), and wherein each global agent is adapted to determine an agent anomaly status (<NUM>) (<NUM>) for a mode or a region of operation of the cyber physical system and a confidence score (<NUM>/<NUM>) for the agent anomaly status (<NUM>).