Multi-Agent Anomaly Detection for Cyber-Physical System Isolation
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Solution Overview
Problem
Industrial control systems are vulnerable to cyber-attacks that can disrupt operations and cause catastrophic damage, with existing methods failing to detect stealthy attacks and multiple simultaneous faults effectively, especially in complex dynamic environments.
Innovation Solution
A system comprising real-time monitoring nodes, a local status determination module with ensemble agents, and a global status determination module with ensemble agents, which generate feature vectors from monitoring node signal values to compare with decision boundaries for anomaly detection and alert generation, providing automatic and accurate protection against cyber-attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single classifier model is used for anomaly detection, then the system is simpler to implement, but it cannot meet the requirements for rapid and accurate abnormality detection and localization in complex dynamic environments
Solution Approach 1:
The system divides the anomaly detection task into multiple specialized agents (global anomaly detection agent, local anomaly detection agent, anomaly localization agent) that each handle specific aspects of detection. This segmentation allows each agent to be optimized for its specific function while collectively achieving high accuracy in complex environments.
Solution Approach 2:
The system merges multiple agents into a unified multi-agent framework that works collaboratively. The global agent, local agent, and localization agent are combined in an ensemble structure that integrates their outputs to achieve both rapid detection and precise localization, resolving the contradiction between simplicity and accuracy.
2Reliability
If multiple simultaneous attacks and faults occur, then the system faces more complex threat scenarios, but existing failure and diagnostics technologies are not designed for detecting stealthy attacks in an automatic manner
Solution Approach 1:
The multi-agent system is designed to handle multiple types of threats simultaneously - cyber-attacks, sensor faults, actuator failures - through a unified detection framework. Each agent can process different types of anomalies, making the system adaptable to various threat scenarios while maintaining reliable protection.
Solution Approach 2:
The system dynamically adapts to different threat scenarios by having agents that can adjust their detection strategies based on the situation. The ensemble of agents can respond to multiple simultaneous attacks and faults by distributing the detection workload and coordinating their responses, providing both reliability and adaptability.
3Measurement precision
If only one monitoring node is used in a detection algorithm, then the system is simpler, but subtle consequences of cyber-attacks such as stealthy attacks might not be readily detectable
Solution Approach 1:
The system segments the monitoring function across multiple specialized agents rather than relying on a single monitoring node. The global agent monitors overall system anomalies, the local agent monitors specific node anomalies, and the localization agent identifies attack origins, collectively enhancing stealthy attack detection capability.
Solution Approach 2:
The multi-agent system introduces intermediary processing layers between the monitoring nodes and the detection output. The agents act as intermediaries that analyze and interpret data from multiple nodes, enabling the system to detect subtle stealthy attacks that would be missed by a single monitoring node while managing complexity through structured intermediary processing.
Data Source
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AI summary
According to some embodiments, a 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 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. Numerous other aspects are provided.