FSM-Based ICS Anomaly Detection With Active PLC Querying
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Solution Overview
Problem
Existing anomaly detection systems in industrial control systems face challenges in adapting to complex and dynamic environments, struggling to accurately and timely identify malicious attacks and system changes, with passive monitoring leading to long response times and inadequate user responsiveness.
Innovation Solution
Anomaly behavior analysis and detection system utilizing a finite state machine (FSM) with active querying, data collection from sensors and PLCs, adaptive learning, and response execution to dynamically adapt to system changes and enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If passive monitoring is used for anomaly detection, then system stability is maintained, but response time increases and detection accuracy decreases
Solution Approach 1:
The patent inverts the traditional passive monitoring approach by implementing active querying of PLC states. Instead of waiting for anomalies to manifest and be detected, the system proactively queries the control system to check for abnormal conditions, thereby reducing response time while maintaining system stability through controlled interrogation rather than continuous monitoring
Solution Approach 2:
The system performs preliminary actions by pre-defining normal state transition patterns and using adaptive learning to establish baseline behaviors before anomalies occur. This allows the system to quickly compare actual states against expected patterns, enabling faster detection without requiring continuous intensive monitoring that would impact system stability
2Reliability
If traditional anomaly detection methods are used, then basic protection is provided, but detection accuracy and timeliness are insufficient for complex attacks
Solution Approach 1:
The patent implements feedback mechanisms through adaptive learning that continuously updates the understanding of normal system behavior based on historical data. The system learns from past operations and adjusts its anomaly detection thresholds dynamically, improving detection accuracy for complex attacks while maintaining the basic protection framework of traditional methods
Solution Approach 2:
The system combines multiple detection approaches into a composite solution: traditional rule-based anomaly detection is integrated with adaptive machine learning models that analyze PLC state transitions. This composite approach leverages the reliability of traditional methods for basic protection while adding the precision of adaptive learning for detecting sophisticated attacks
3Adaptability or versatility
If adaptive learning is implemented to improve detection accuracy, then system adaptability increases, but device complexity increases
Solution Approach 1:
The patent segments the adaptive learning functionality into a separate anomaly detection module that operates independently from the core PLC control logic. This modular segmentation allows adaptive learning algorithms to be implemented without complicating the main control system, as the learning component can be developed, updated, and maintained separately while providing enhanced detection capabilities
Data Source
AI summary
The present application provides an anomaly detection system for industrial control systems, encompassing data collection, data processing, anomaly detection, and response execution modules. It uniquely employs a strategy based on Finite State Machines (FSM), actively querying the data collection module to gather operational data from sensors and Programmable Logic Controllers (PLC). The data processing module standardizes and formats the data, while the anomaly detection module uses a predefined FSM model and adaptive learning mechanisms to enhance the accuracy of anomaly identification.