General Electric Company

Ge Infrastructure Technology, LLC

General Electric Technology Gmbh

General Electric Renovables Espana, S.l.

Ge Vernova Infrastructure Technology LLC

Ge Digital Holdings LLC

Masoud Abbaszadeh

Lalit Keshav Mestha

Weizhong Yan

Justin Varkey John

Mustafa Tekin Dokucu

Daniel Francis Holzhauer

Fernando Javier D'Amato

Cody Joe Bushey

Hema Kumari Achanta

Matthew Christian Nielsen

Subhrajit Roychowdhury

Honggang Wang

Olugbenga Moses Anubi

Austars Raymond Schnore, Jr

Chaitanya Ashok Baone

Reza Ghaemi

Fabiano Daher Adegas

Walter Charles Yund

Annarita Giani

Georgios Boutselis

Pierino Gianni Bonanni

Sumit Bose

Balakrishna Pamulaparthy

Kalpesh Singal

Conner Brooks Shane

Naresh Acharya

Justin Barton

Santosh Sambamoorthy Veda

Saikat Ray Majumder

Rui Xu

Mitalkumar Kanabar

Souransu Nandi

Yuh-Shyang Wang

Junqiang Zhou

Willard Monten Wiseman

Karla Kvaternik

Pubudu Eroshan Weerathunga

Zhaoyuan Yang

Cody Bushy

Annartia Giani

Fernando Javier D´Amato

According to some embodiments, a system, method and non-transitory computer readable medium are provided 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 to determine an anomaly status for one or more nodes; a global status determination module to determine an anomaly status for the cyber physical system; a threat detection computer platform to: receive the monitoring node signal values, generate feature vectors from the values; compare the feature vectors with at least one decision boundary associated with each of a local abnormal detection model and 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. Numerous other aspects are provided.

Unified multi-agent system for abnormality detection and isolation

Systems and methods for power system switching element (PSSE) anomaly detection are disclosed. An example PSSE anomaly detection unit may include a power system switching element position estimator (PSSEPE) and a comparison unit. The PSSEPE may be configured to receive a set of measurements and a set of control commands associated with a PSSE, calculate an anomaly confidence score based on the set of measurements and the set of control commands, and estimate a calculated PSSE position based on the set of measurements and the set of control commands. The comparison unit may be configured to receive the calculated PSSE position from the PSSEPE, receive the set of measurements and the set of control commands from the PSSEPE, receive a reported PSSE position associated with the PSSE, and determine a PSSE anomaly decision based on a difference between the reported PSSE position and the calculated PSSE position.

Systems and methods for power system switching element anomaly detection

A method for controlling a distributed power system is provided, the system including an aggregator communicatively coupled to a plurality of nodes, each of the plurality of nodes including an associated load. The method includes receiving, at the aggregator, a commanded power profile from an independent service operator, the commanded power profile including a commanded power deviation for the distributed power system, calculating, using the aggregator, a score for each of the plurality of nodes based on a current operating power of each node, selecting, using the aggregator, a subset of the plurality of nodes based on the calculated scores, and commanding, using the aggregator, each node in the subset to adjust its current power by a respective predetermined amount, the predetermined amounts determined based on the commanded power deviation.

Systems and methods for rapidly responding to commanded power profiles

According to some embodiments, a system, method and non-transitory computer-readable medium are provided to protect a cyber-physical system having a plurality of monitoring nodes comprising: a normal space data source storing, for each of the plurality of monitoring nodes, a series of normal monitoring node values over time that represent normal operation of the cyber-physical system; a situational awareness module including an abnormal data generation platform, wherein the abnormal data generation platform is operative to generate abnormal data to represent abnormal operation of the cyber-physical system using values in the normal space data source and a generative model; a memory for storing program instructions; and a situational awareness processor, coupled to the memory, and in communication with the situational awareness module and operative to execute the program instructions to: receive a data signal, wherein the received data signal is an aggregation of data signals received from one or more of the plurality of monitoring nodes, wherein the data signal includes at least one real-time stream of data source signal values that represent a current operation of the cyber-physical system; determine, via a trained classifier, whether the received data signal is a normal signal or an abnormal signal, wherein the trained classifier is trained with the generated abnormal data and normal data; localize an origin of an anomaly when it is determined the received data signal is the abnormal signal; receive the determination and localization at a resilient estimator module; execute the resilient estimator module to generate a state estimation for the cyber-physical system. Numerous other aspects are provided.

Resilient estimation for grid situational awareness

The present application describes techniques for node selection and ranking for, e.g., attack detection and localization in cyber-physical systems, without relying on digital twins, computer models of assets, or operational domain expertise. The described techniques include obtaining an input dataset of values for a plurality of nodes (e.g., sensors, actuators, controllers, software nodes) of industrial assets, computing a plurality of principal components (PCs) for the input dataset according to variance of values for each node, computing a set of common weighted PCs based on the plurality of PCs according to variance of each PC, and ranking each node based on the node's contribution to the set of common weighted PCs.

Systems and methods for node selection and ranking in cyber-physical systems

A method for detecting a cyberattack on a control system of a wind turbine includes providing a plurality of classification models of the control system. The method also includes receiving, via each of the plurality of classification models, a time series of operating data from one or more monitoring nodes of the wind turbine. The method further includes extracting, via the plurality of classification models, a plurality of features using the time series of operating data. Each of the plurality of features is a mathematical characterization of the time series of operating data. Moreover, the method includes generating an output from each of the plurality of classification models and determining, using a decision fusion module, a probability of the cyberattack occurring on the control system based on a combination of the outputs. Thus, the method includes implementing a control action when the probability exceeds a probability threshold.

System and method for cyberattack detection in a wind turbine control system

According to some embodiments, a system, method, and non-transitory computer readable medium are provided 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; and a threat detection computer platform, coupled to the plurality of real-time monitoring nodes, to: receive the monitoring node signal values; compute an anomaly score; compare the anomaly score with an adaptive threshold; and detect that one of a particular monitoring node and a system is outside a decision boundary based on the comparison, and classify that particular monitoring node or system as anomalous. Numerous other aspects are provided.

Attack detection and localization with adaptive thresholding

Systems and methods are provided for the control of an industrial asset, such as a power generating asset. Accordingly, a cyber-attack model predicts a plurality of operational impacts on the industrial asset resulting from a plurality of potential cyber-attacks. The cyber-attack model also predicts a corresponding plurality of potential mitigation responses. In operation, a cyber-attack impacting at least one component of the industrial asset is detected via the cyber-attack neutralization module and a protected operational impact of the cyber-attack is identified based on the cyber-attack model. The cyber-attack neutralization module selects at least one mitigation response of the plurality of mitigation responses based on the predicted operational impact and an operating state of the industrial asset is altered based on the selected mitigation response.

Systems and methods for controlling an industrial asset in the presence of a cyber-attack

An industrial asset may have monitoring nodes that generate current monitoring node values representing a current operation of the industrial asset. An abnormality detection computer may detect when a monitoring node is currently being attacked or experiencing a fault based on a current feature vector, calculated in accordance with current monitoring node values, and a detection model that includes a decision boundary. A model updater (e.g., a continuous learning model updater) may determine an update time-frame (e.g., short-term, mid-term, long-term, etc.) associated with the system based on trigger occurrence detection (e.g., associated with a time-based trigger, a performance-based trigger, an event-based trigger, etc.). The model updater may then update the detection model in accordance with the determined update time-frame (and, in some embodiments, continuous learning).

Systems and methods for global cyber-attack or fault detection model

An industrial asset may have monitoring nodes that generate current monitoring node values. A dynamic, resilient estimator may split a temporal monitoring node space into normal and one or more abnormal subspaces associated with different kinds of attack vectors. According to some embodiments, a neutralization model is constructed and trained for each attack vector using supervised learning and the associated abnormal subspace. In other embodiments, a single model is created using out-of-range values for abnormal monitoring nodes. Responsive to an indication of a particular abnormal monitoring node or nodes, the system may automatically invoke the appropriate neutralization model to determine estimated values of the particular abnormal monitoring node or nodes (e.g., by selecting the correct model or using out-of-range values). The series of current monitoring node values from the abnormal monitoring node or nodes may then be replaced with the estimated values.

Virtual sensor supervised learning for cyber-attack neutralization

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.

Dynamic physical watermarking for attack detection in cyber-physical systems

In some embodiments, identifying a replay attack in an industrial control system of an industrial asset includes receiving a first set of time series data associated with an ambient condition of one or more first monitoring nodes at a first location of the industrial control system. An actual system feature value for the industrial asset is determined based upon the first set of time series data. A second set of time series data indicative of the ambient condition at a second location is received, and a nominal system feature value is determined based upon the second set of time series data. A correlation between the actual feature value and the nominal system feature value is analyzed to determine a correlation result. A request received by the industrial control system is selectively categorized as a replay attack based upon the correlation result.

Non-intrusive replay attack detection system

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., an attestation blockchain).

Framework for cyber-physical system protection of electric vehicle charging stations and power grid

A cyber-physical system may have monitoring nodes that generate a series of current monitoring node values over time that represent current operation of the system. A hierarchical abnormality localization computer platform accesses a multi-level hierarchy of elements, and elements in a first level of the hierarchy are associated with elements in at least one lower level of the hierarchy and at least some elements may be associated with monitoring nodes. The computer platform may then determine, based on feature vectors and a decision boundary, an abnormality status for a first element in the highest level of the hierarchy. If the abnormality status indicates an abnormality, the computer platform may determine an abnormality status for elements, associated with the first element, in at least one level of the hierarchy lower than the level of the first element. These determinations may be repeated until an abnormality is localized to a monitoring node.

Scalable hierarchical abnormality localization in cyber-physical systems

Growing community of inventors

Clifton Park, NY, United States of America

Masoud Abbaszadeh