Abstract: Some embodiments provide a system to protect an electric vehicle charging infrastructure. An electric vehicle charging site may receive AC power from a power grid and provide DC power to electric vehicles. The charging site may include a plurality of monitoring nodes each generating a series of current monitoring node values over time that represent a current operation of the electric vehicle charging infrastructure. A supply equipment communication controller may receive an access request from an access requestor associated with an electric vehicle, the access request being associated with a platform certificate. A secondary actor policy decision point at the charging site may evaluate the access requestor's identity and respond with an action message allowing high-level communication with the access requestor to proceed. Note that information associated with the current monitoring node values and/or the access request may be stored in a secure, distributed transaction ledger (e.g.

Abstract: According to some embodiments, a plurality of monitoring nodes may each generate a series of current monitoring node values over time that represent a current operation of the industrial asset. A node classifier computer, coupled to the plurality of monitoring nodes, may receive the series of current monitoring node values and generate a set of current feature vectors. The node classifier computer may also access at least one multi-class classifier model having at least one decision boundary. The at least one multi-class classifier model may be executed and the system may transmit a classification result based on the set of current feature vectors and the at least one decision boundary. The classification result may indicate, for example, whether a monitoring node status is normal, attacked, or faulty.

Abstract: In some embodiments, a system model construction platform may receive, from a system node data store, system node data associated with an industrial asset. The system model construction platform may automatically construct a data-driven, dynamic system model for the industrial asset based on the received system node data. A synthetic attack platform may then inject at least one synthetic attack into the data-driven, dynamic system model to create, for each of a plurality of monitoring nodes, a series of synthetic attack monitoring node values over time that represent simulated attacked operation of the industrial asset. The synthetic attack platform may store, in a synthetic attack space data source, the series of synthetic attack monitoring node values over time that represent simulated attacked operation of the industrial asset. This information may then be used, for example, along with normal operational data to construct a threat detection model for the industrial asset.

Abstract: Systems and methods may be associated with a cyber-physical system, and a blueprint repository data store may contain electronic files that represent behavior-based asset monitoring parameters for different cyber-physical system asset types. A behavior-based asset monitoring creation computer platform may receive an indication of an asset type of the cyber-physical system. The behavior-based asset monitoring creation computer platform may then search the blueprint repository data store and retrieve an electronic file representing behavior-based asset monitoring parameters for the asset type of the cyber-physical system to be monitored. The behavior-based asset monitoring creation computer platform may also receive, from the remote operator device, adjustments to the retrieved behavior-based asset monitoring parameters and automatically configure, based on the adjusted behavior-based asset monitoring parameters, at least a portion of settings for an abnormal detection model.

Abstract: A cyber-physical system may have 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. According to some embodiments, a features extraction computer platform may receive the series of current monitoring node values over time and generate current feature vectors based on the series of current monitoring mode values. A system mode estimation computer platform may provide the current feature vectors to a probabilistic graphical model to generate an estimated system mode. The system mode estimation computer platform may then compare the estimated system mode with a currently reported system mode output by the cyber-physical system and generate a system mode status indication based on a result of said comparison. According to some embodiments, the system mode status indication can be used to override the currently reported system mode of the cyber-physical system.

Abstract: A cyber-physical system may have 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. According to some embodiments, a watermarking computer platform may randomly inject a watermarking signal into an injection subset of the system nodes. The watermarking computer platform may then receive current monitoring node values over time and generate a current watermarking feature vector based on the current monitoring node values. The watermarking computer platform might comprise a dedicated watermarking abnormality detection platform or a unified abnormality detection platform (e.g., that also uses data-drive feature vectors). The injection subset may be associated with a randomly selected subset of the system nodes and/or magnitudes of watermarking signals that are randomly selected.

Abstract: A cyber-physical system may have a plurality of monitoring nodes each generating a series of current monitoring node values over time representing current operation of the system. A data-driven features extraction computer platform may receive the series of current monitoring node values and generate current data-driven feature vectors based on the series of current monitoring node values. A residual features extraction computer platform may receive the series of current monitoring node values, execute a system model and utilize a stochastic filter to determine current residual values, and generate current residual-driven feature vectors based on the current residual values. An abnormal detection platform may then receive the current data-driven and residual-driven feature vectors and compare the current data-driven and residual-driven feature vectors with at least one decision boundary associated with an abnormal detection model.

Abstract: Monitoring nodes may generate a series of current monitoring node values over time representing current operation of a cyber-physical system. A decision fusion computer platform may receive, from a local status determination module, an indication of whether each node has an initial local status of “normal”/“abnormal” and a local certainty score (with higher values of the local certainty score representing greater likelihood of abnormality). The computer platform may also receive, from a global status determination module, an indication of whether the system has an initial global status of “normal”/“abnormal” and a global certainty score. The computer platform may output, for each node, a fused local status of “normal” or “abnormal,” at least one fused local status being based on the initial global status. The decision fusion computer platform may also output a fused global status of “normal” or “abnormal” based on at least one initial local status.

Abstract: A plurality of monitoring nodes may each generate a time-series of current monitoring node values representing current operation of a cyber-physical system. A feature-based forecasting framework may receive the time-series of and generate a set of current feature vectors using feature discovery techniques. The feature behavior for each monitoring node may be characterized in the form of decision boundaries that separate normal and abnormal space based on operating data of the system. A set of ensemble state-space models may be constructed to represent feature evolution in the time-domain, wherein the forecasted outputs from the set of ensemble state-space models comprise anticipated time evolution of features. The framework may then obtain an overall features forecast through dynamic ensemble averaging and compare the overall features forecast to a threshold to generate an estimate associated with at least one feature vector crossing an associated decision boundary.

Abstract: According to some embodiments, a plurality of monitoring nodes each generate a series of current monitoring node values over time that represent a current operation of a wind turbine. An abnormality detection computer platform may receive the series of current monitoring node values and generate a set of current feature vectors. The abnormality detection computer platform may also access an abnormality detection model having a plurality of decision boundaries created using wind information (e.g., wind speed and/or acceleration) along with at least one of a set of normal feature vectors and a set of abnormal feature vectors. The abnormality detection computer platform may then select one of the decision boundaries based on current wind information associated with the wind turbine and execute the abnormality detection model and transmit an abnormality alert signal based on the set of current feature vectors and the selected decision boundary.

Abstract: A system to protect a fleet of industrial assets may include a communication port to exchange information with a plurality of remote industrial assets. An industrial fleet protection system may receive information from the plurality of remote industrial assets or a cloud-based security platform and calculate, based on information received from multiple industrial assets, a current fleet-wide operation feature vector. The industrial fleet protection system may then compare the current fleet-wide operation feature vector with a fleet-wide decision boundary (e.g., separating normal from abnormal operation of the industrial fleet). The system may then automatically transmit a response (e.g., a cyber-attack threat alert or an adjustment to a decision boundary of an industrial asset) when a result of the comparison indicates abnormal operation of the industrial fleet.

Abstract: According to some embodiments, streams of monitoring node signal values may be received over time that represent a current operation of an industrial asset control system. A current operating mode of the industrial asset control system may be received and used to determine a current operating mode group from a set of potential operating mode groups. For each stream of monitoring node signal values, a current monitoring node feature vector may be determined. Based on the current operating mode group, an appropriate decision boundary may be selected for each monitoring node, the appropriate decision boundary separating a normal state from an abnormal state for that monitoring node in the current operating mode. Each generated current monitoring node feature vector may be compared with the selected corresponding appropriate decision boundary, and a threat alert signal may be automatically transmitted based on results of said comparisons.

Abstract: The technology described herein is generally directed towards a distributed optimization technology for the control of aggregation of distributed flexibility resource nodes that operates iteratively until a commanded power profile is produced by aggregated loads. The technology uses a distributed iterative solution in which each node solves a local optimization problem with local constraints and states, while using a global Lagrange multiplier that is based upon information from each other node. The global Lagrange multiplier is determined at an aggregation level using load-specific information that is obtained in a condensed form (e.g., a scalar) from each node at each iteration. The global Lagrange multiplier is broadcasted to the nodes for each new iteration. The technology provides an iterative, distributed solution to the network optimization problem of power tracking of aggregated loads.

Abstract: According to some embodiments, a plurality of heterogeneous data source nodes may each generate a series of current data source node values over time that represent a current operation of an electric power grid. A real-time threat detection computer, coupled to the plurality of heterogeneous data source nodes, may receive the series of current data source node values and generate a set of current feature vectors. The threat detection computer may then access an abnormal state detection model having at least one decision boundary created offline using at least one of normal and abnormal feature vectors. The abnormal state detection model may be executed, and a threat alert signal may be transmitted if appropriate based on the set of current feature vectors and the at least one decision boundary.

Abstract: According to some embodiments, a threat detection computer platform may receive a plurality of real-time monitoring node signal values over time that represent a current operation of the industrial asset. For each stream of monitoring node signal values, the platform may generate a current monitoring node feature vector. The feature vector may also be estimated using a dynamic model output with that monitoring node signal values. The platform may then compare the feature vector with a corresponding decision boundary for that monitoring node, the decision boundary separating a normal state from an abnormal state for that monitoring node. The platform may detect that a particular monitoring node has passed the corresponding decision boundary and classify that particular monitoring node as being under attack. The platform may then automatically determine if the attack on that particular monitoring node is an independent attack or a dependent attack.

Abstract: According to some embodiments, streams of monitoring node signal values may be received over time that represent a current operation of an industrial asset control system. A current operating mode of the industrial asset control system may be received and used to determine a current operating mode group from a set of potential operating mode groups. For each stream of monitoring node signal values, a current monitoring node feature vector may be determined. Based on the current operating mode group, an appropriate decision boundary may be selected for each monitoring node, the appropriate decision boundary separating a normal state from an abnormal state for that monitoring node in the current operating mode. Each generated current monitoring node feature vector may be compared with the selected corresponding appropriate decision boundary, and a threat alert signal may be automatically transmitted based on results of said comparisons.

Abstract: Streams of monitoring node signal values over time, representing a current operation of the industrial asset, are used to generate current monitoring node feature vectors. Each feature vector is compared with a corresponding decision boundary separating normal from abnormal states. When a first monitoring node passes a corresponding decision boundary, an attack is detected and classified as an independent attack. When a second monitoring node passes a decision boundary, an attack is detected and a first decision is generated based on a first set of inputs indicating if the attack is independent/dependent. From the beginning of the attack on the second monitoring node until a final time, the first decision is updated as new signal values are received for the second monitoring node. When the final time is reached, a second decision is generated based on a second set of inputs indicating if the attack is independent/dependent.

Abstract: An industrial asset may be associated with a plurality of monitoring nodes, each monitoring node generating a series of monitoring node values over time representing current operation of the industrial asset. An abnormality detection computer may determine that at least one abnormal monitoring node is currently being attacked or experiencing a fault. A virtual sensing estimator may continuously execute an adaptive learning process to create or update virtual sensor models for the monitoring nodes. Responsive to an indication that a monitoring node is currently being attacked or experiencing a fault, the virtual sensing estimator may be dynamically reconfigured to estimate a series of virtual node values for the abnormal monitoring node or nodes based on information from normal monitoring nodes and appropriate virtual sensor models. The series of monitoring node values from the abnormal monitoring node or nodes may then be replaced with the virtual node values.

Abstract: According to some embodiments, a plurality of monitoring nodes may each generate a series of current monitoring node values over time that represent a current operation of the industrial asset. A node classification computer may determine, for each monitoring node, a classification result indicating whether each monitoring node is in a normal or abnormal state. A disambiguation engine may receive the classification results from the node classification computer and associate a Hidden Markov Model (“HMM”) with each monitoring node. For each node in an abnormal state, the disambiguation engine may execute the HMM associated with that monitoring node to determine a disambiguation result indicating if the abnormal state is a result of an attack or a fault and output a current status of each monitoring node based on the associated classification result and the disambiguation result.

Abstract: In some embodiments, a plurality of monitoring nodes each generate a series of current monitoring node values over time that represent a current operation of the industrial asset. An attack detection computer platform may receive the series of current monitoring node values and generate a set of current feature vectors including a current feature for capturing transients (e.g., local transients and/or global transients). The attack detection computer platform may also access an attack detection model having at least one decision boundary that was created using at least one of a set of normal feature vectors and/or a set of attacked feature vectors. The attack detection model may then be executed such that an attack alert signal is transmitted by the attack detection computer platform, when appropriate, based on the set of current feature vectors (including the current feature to capture transients) and the at least one decision boundary.