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Anomaly Detection Method for Industrial Control System Operation Data Based on Time-Frequency Fusion Feature

Jiayi Liu¹, Yun Sha¹, Wenchang Zhang¹

¹Information Engineering College, Beijing Institute of Petrochemical Technology, Beijing 102617, China.

Sensors (Basel, Switzerland)

|September 28, 2024

Summary

This study introduces TFANet, a novel method for anomaly detection in industrial control systems (ICS). TFANet effectively extracts features from time and frequency domains, significantly improving anomaly detection accuracy in ICS data.

Keywords:

anomaly detection attention mechanism feature fusion industrial control security sensor operation data

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Area of Science:

Computer Science
Cybersecurity
Signal Processing

Background:

Industrial Control Systems (ICS) require robust security monitoring.
ICS data presents challenges due to complexity, multi-dimensionality, and long-sequence time-series characteristics.
Existing anomaly detection methods struggle with ICS data's periodic variations and complex temporal associations.

Purpose of the Study:

To propose an advanced anomaly detection method for ICS data.
To address limitations in feature extraction for complex ICS time-series data.
To enhance the security monitoring capabilities of ICS.

Main Methods:

Developed TFANet (Time-Frequency Fusion Feature Attention Network).
Transformed time-domain ICS data into the frequency domain (amplitude and phase).
Extracted features from both time and frequency domains, focusing on temporal changes and associations.
Fused six learned features and employed an attention mechanism for feature weighting and anomaly classification.

Main Results:

TFANet demonstrated superior performance on three ICS datasets compared to iTransformer, Crossformer, and TimesNet.
Achieved significant average improvements across accuracy (19%), precision (37%), recall (31%), F1 score (35%), and AUC-ROC (22%).
Effectively handled complex periodic characteristics and long-distance temporal dependencies in ICS data.

Conclusions:

TFANet offers a powerful and effective approach for anomaly detection in ICS.
The time-frequency fusion and attention mechanism are key to TFANet's enhanced performance.
This method significantly advances the state-of-the-art in ICS security monitoring.