ICS Signal Interdependency Modeling for Automated Anomaly Detection
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Solution Overview
Problem
Current industrial control system (ICS) management systems are inadequate in detecting anomalies in raw sensor data due to limited monitoring capabilities and the processing of data using Digital Signal Processing techniques, which fail to capture dependencies and correlations between signals.
Innovation Solution
A fully automated anomaly detection system that uses a behavioral model to analyze interdependency-based and behavior-based groups of ICS signals, detecting anomalies by monitoring conformance with predicted interdependencies and statistical behaviors, and updating the model upon repeated nonconformities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Digital Signal Processing techniques are used to process sensor data, then the data can be filtered and normalized, but dependencies and correlations between raw sensor signals are not captured
Solution Approach 1:
The system performs preliminary clustering of sensor signals into interdependency-based groups before anomaly detection, capturing signal relationships in advance. This allows the system to analyze correlations between raw signals while still applying filtering and processing techniques, thereby preserving information that would otherwise be lost.
Solution Approach 2:
The sensor data is segmented into multiple interdependency-based clusters according to signal relationships. By dividing the data into meaningful groups that preserve correlations, the system can apply processing techniques to each cluster while maintaining the dependency structures within and between clusters.
2Reliability
If current management systems monitor sensor data, then some anomalies can be detected, but only a small percentage of sensor data is monitored and many anomalies are missed
Solution Approach 1:
The monitoring system segments sensor signals into interdependency-based clusters, allowing comprehensive monitoring of all sensor data through organized groups. This segmentation enables the system to handle large volumes of data systematically, improving coverage without proportionally increasing complexity.
Solution Approach 2:
The system uses unsupervised learning algorithms that continuously learn from sensor data patterns and provide feedback to improve anomaly detection. The behavioral models are updated based on observed patterns, enabling the system to adapt and improve detection coverage over time without linear increases in complexity.
3Reliability
If a behavioral model with interdependency-based groups is used, then comprehensive anomaly detection is achieved, but the system requires automated model updating upon repeated nonconformities
Solution Approach 1:
The behavioral model performs self-service by automatically updating itself when repeated nonconformities are detected. The unsupervised learning algorithms autonomously adjust the model parameters and cluster assignments based on observed patterns, eliminating the need for manual intervention while maintaining comprehensive anomaly detection capabilities.
Solution Approach 2:
The system implements feedback loops where detection results feed back into model updating. When anomalies are repeatedly detected in specific patterns, the feedback triggers automated model adjustments, creating a self-improving system that enhances comprehensiveness through automated adaptation.
Data Source
AI summary
A system and method for automatically detecting anomalies in an industrial control system (ICS) is provided. A behavioral model is provided, the model comprising groups of learned sets of interdependent ICS signals of parameters associated with an operation of the ICS. For each of the groups, the learned sets in the respective group include at least one independent signal and one or more dependent signals that are dependent on the independent signal in accordance with a common type of dependency. Monitoring signals of given parameters are obtained, the monitoring signals corresponding to a given learned set of the learned sets in one of the groups. Upon determining a nonconformance of an observed interdependency of the monitoring signals with a predicted interdependency of the monitoring signals, the predicted interdependency being in accordance with the type of dependency associated with the given learned set, an anomaly is automatically detected.