TSMixAD: A Time-Series Anomaly Detection Framework for Industrial Control Systems Incorporating Time-Frequency Domain Data Augmentation Techniques

Abstract

Industrial control systems are an important part of the nation’s critical infrastructure, and effective anomaly detection is important to ensure system safety. However, the challenges of anomaly sample scarcity and unequal data distribution in industrial control systems limit the effectiveness of existing anomaly detection methods. Therefore, this paper proposes a time-series anomaly detection framework, TSMixAD, which integrates time-frequency domain enhancement. The Mixup method based on Dirichlet distribution is adopted in the time domain, and frequency mask, noise injection and frequency shift enhancement are adopted in the frequency domain to improve the classification ability of a sample. Tcn-transformer hybrid encoder is constructed, in which TCN is responsible for extracting local time dependencies efficiently, and Transformer is responsible for global association modeling, so as to improve the robustness of anomaly detection. We validate our approach on two publicly available industrial control system datasets, SWaT and WADI.