Neural Network Consensus for Control System Anomaly Detection
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Solution Overview
Problem
Control systems, particularly in industrial and manufacturing facilities, face challenges in detecting anomalies due to complex state spaces and the risk of cyberattacks, which can disrupt critical processes without detection, given the use of open communication protocols and diverse vendor components.
Innovation Solution
Implementing a computer-implemented method using neural networks for anomaly detection, where sensor data and network data are normalized and aligned, then fed into separate classifiers to identify discrepancies, indicating potential anomalies, such as cyberattacks or faulty equipment, by comparing classified states for consensus.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If open communication protocols and diverse vendor components are used in control systems, then adaptability and interoperability are improved, but security and reliability deteriorate due to increased vulnerability to cyberattacks
Solution Approach 1:
The system performs preliminary anomaly detection by continuously monitoring control system data against learned normal behavior patterns before cyberattacks can cause significant damage. The neural network models detect deviations from expected system states in advance, enabling preventive security measures.
Solution Approach 2:
The patent introduces an intermediary anomaly detection system that sits between the control system components and external threats. This intermediary layer analyzes system behavior and flags suspicious patterns, acting as a security buffer that protects the control system while maintaining open protocol interoperability.
2Measurement precision
If comprehensive monitoring of control system behavior is implemented, then anomaly detection capability is improved, but system complexity increases
Solution Approach 1:
The patent replaces complex manual monitoring and analysis systems with automated neural network models. These AI-based systems automatically learn normal system behavior patterns and detect anomalies without requiring complex rule-based monitoring infrastructure, reducing overall system complexity while improving detection precision.
Solution Approach 2:
The system creates virtual copies of normal control system behavior through trained neural network models. These digital twins serve as reference patterns for comparison, enabling precise anomaly detection without requiring physical duplication of monitoring hardware or complex analysis systems.
3Reliability
If real-time anomaly detection is implemented, then system safety and reliability are improved, but computational resources and processing time increase
Solution Approach 1:
The system implements partial monitoring by focusing computational resources on detecting specific types of anomalies and critical system parameters. Rather than analyzing every single data point in real-time, the neural networks are trained to identify the most significant deviation patterns, reducing computational overhead while maintaining safety.
Solution Approach 2:
The neural network models are trained offline in advance on historical control system data to learn normal behavior patterns. This preliminary training phase shifts computational burden away from real-time operation, allowing the system to perform lightweight anomaly detection during actual control operations with minimal energy consumption.
Data Source
AI summary
Described herein are methods, systems, and platforms comprising neural networks for control system anomaly detection.