Temporal Graph Anomaly Control for Cyber-Physical Root Cause Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional anomaly detection systems in Cyber-Physical Systems (CPS) are inadequate for real-time monitoring and corrective actions due to their inability to handle categorical sensor data and relationships between sensors, leading to difficulties in identifying root causes and prioritizing critical components.
Innovation Solution
A Temporal Graph-based Incident Analysis System (TGIAS) and Transition Based Categorical Anomaly Detection (TCAD) method that preprocesses multimodal data from distributed sensors, constructs incident timeline graphs, calculates severity scores, and identifies probable root causes and anomaly propagation pathways, enabling real-time corrective actions and system optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional anomaly detection systems are used for CPS monitoring, then system simplicity is maintained, but the ability to detect anomalies in categorical sensor data and identify sensor relationships is lost
Solution Approach 1:
The patent transforms categorical sensor data into numerical representations by encoding categorical values and constructing transition probability matrices. This parameter transformation enables conventional numerical anomaly detection algorithms to process categorical data effectively, resolving the contradiction between detection reliability and adaptability to categorical data types.
Solution Approach 2:
The patent introduces transition probability matrices as an intermediary between categorical sensor data and anomaly detection algorithms. These matrices capture the temporal relationships and transitions in categorical data, serving as a bridge that allows numerical detection methods to analyze categorical inputs, thereby improving both detection capability and data type versatility.
2Reliability
If a large number of sensors are deployed in CPS, then monitoring coverage is improved, but the complexity of determining relationships between sensors increases
Solution Approach 1:
The patent segments the complex sensor network into individual sensor units, each with its own transition probability matrix. By analyzing each sensor's categorical transitions independently and then aggregating results, the system maintains comprehensive monitoring coverage while avoiding the combinatorial explosion of analyzing all sensor relationships simultaneously, thus reducing overall system complexity.
Solution Approach 2:
The patent creates simplified copies of sensor behavior through transition probability matrices that represent each sensor's categorical transitions. These matrix representations serve as computationally efficient proxies for the actual sensors, allowing relationship analysis to be performed on the simplified models rather than the full complex sensor data, thereby reducing analytical complexity while maintaining monitoring effectiveness.
3Speed
If real-time anomaly detection is implemented in CPS, then incident response time is reduced, but the computational complexity of processing sensor data increases
Solution Approach 1:
The patent performs preliminary encoding of categorical sensor data into transition probability matrices during normal operation, establishing baseline transition patterns before anomalies occur. This pre-processing and baseline establishment enables real-time anomaly detection to simply compare current transitions against pre-computed baselines, significantly reducing the computational complexity required for real-time processing while maintaining fast incident response.
Data Source
AI summary
Systems and methods are provided for incident analysis in Cyber-Physical Systems (CPS) using a Temporal Graph-based Incident Analysis System (TGIAS) and/or Transition Based Categorical Anomaly Detection (TCAD). Dynamically gathered multimodal data from a distributed network of sensors across the CPS are preprocessed to identify abnormal sensor readings indicative of potential incidents, and a multi-layered incident timeline graph, representing abnormal sensor readings, relationships to specific CPS components, and temporal sequencing of events is constructed. Severity scores are calculated, and severity rankings are assigned to identified anomalies based on a composite index including impact on CPS operation, comparison with historical incident data, and predictive risk assessments. Probable root causes of incidents and pathways for anomaly propagation through the CPS are identified using causal interference and the incident timeline graph to detect underlying vulnerabilities and predict future system weaknesses. Recommended actions are generated and executed for incident resolution and system optimization.