Industrial Process Threat Detection Using Subsystem Autoencoders
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Solution Overview
Problem
Industrial process systems connected to networks are vulnerable to cyberattacks due to lack of authentication and encryption, leading to potential sabotage and operational disruptions.
Innovation Solution
A system utilizing deep autoencoder (DAE) models is implemented to monitor industrial process systems by constructing and training DAE models for each subsystem, allowing for real-time anomaly detection and identification of cyberattacks based on reconstruction errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If industrial process systems are connected to networks for real-time monitoring and optimization, then productivity and operational efficiency are improved, but vulnerability to cyberattacks increases due to lack of authentication and encryption
Solution Approach 1:
The system performs preliminary actions by training deep autoencoder models on normal operational data before deployment. These models learn the expected behavior patterns of the industrial process system in advance, enabling them to detect anomalies caused by cyberattacks during real-time operation without requiring authentication or encryption modifications
Solution Approach 2:
The patent introduces deep autoencoder models as intermediary components between the network-connected industrial process system and the threat detection function. These models act as unsupervised anomaly detectors that translate system behavior into detectable patterns, enabling security monitoring without modifying the underlying system architecture or adding authentication/encryption layers
2Measurement precision
If deep autoencoder models are trained on normal operational data to detect anomalies, then cyberattack detection accuracy is improved, but false alarms may occur during legitimate operational variations
Solution Approach 1:
The system dynamically adapts by continuously monitoring reconstruction errors and adjusting detection thresholds based on operational context. The deep autoencoder models process varying input patterns and dynamically classify anomalies, allowing the system to distinguish between legitimate operational variations and actual cyberattacks through adaptive decision boundaries
Solution Approach 2:
The system implements feedback mechanisms where reconstruction error patterns are continuously analyzed and fed back into the detection algorithm. This feedback loop enables the system to learn from false alarms and improve discrimination between normal variations and actual threats, reducing false positive rates while maintaining detection sensitivity
3Measurement precision
If the industrial process system is divided into multiple subsystems for monitoring, then detection granularity and precision are improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the industrial process system into multiple independent subsystems, each monitored by dedicated deep autoencoder models. This modular approach enables focused detection on specific process areas while maintaining overall system visibility, balancing granularity with computational efficiency through distributed modeling
Solution Approach 2:
The deep autoencoder models serve multiple functions simultaneously: they perform dimensionality reduction, feature extraction, anomaly detection, and reconstruction error analysis within a single unified framework. This multi-functionality reduces overall system complexity compared to using separate specialized components for each function
Data Source
AI summary
Examples of techniques for threat detection in an industrial process system are described herein. An aspect includes determining a plurality of subsystems of an industrial process system. Another aspect includes, for each of the plurality of subsystems, constructing and training a respective deep autoencoder (DAE) model of the subsystem based on data corresponding to the industrial process system. Another aspect includes monitoring the industrial process system using the plurality of DAE models corresponding to the plurality of subsystems. Another aspect includes, based on the plurality of DAE models, determining a cyberattack in a subsystem of the plurality of subsystems.