NOAE: Noise-Optimized Adversarial Examples for Multivariate Time Series Anomaly Detection of the Industrial Internet of Things

Summary

The paper proposes Noise-Optimized Adversarial Examples (NOAE), an attack method against unsupervised deep-learning multivariate time series anomaly detection models used in the Industrial Internet of Things. Experiments show NOAE significantly reduces detection precision on industrial benchmarks, including a 71.51% precision drop on the SMAP dataset at a 0.01 noise amplitude. The authors also propose a Hybrid Adversarial Defense (HAD) training approach that uses data-end random segment replacement augmentation to improve robustness.

Mitigation

The authors propose Hybrid Adversarial Defense (HAD), a training approach that uses adversarial examples and data-end random segments replacement augmentation to alleviate the weak robustness of anomaly detection models.

Topics

• Adversarial machine learning

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