STFT-TCAN: A TCN-attention based multivariate time series anomaly detection architecture with time-frequency analysis for cyber-industrial systems

Author:

Tu Fei-Fan,Liu Dong-Jie,Yan Zhi-Wei,Jin Xiao-BoORCID,Geng Guang-Gang

Publisher

Elsevier BV

Reference36 articles.

1. Ahmed, C.M., Palleti, V.R., Mathur, A.P., 2017. WADI: a water distribution testbed for research in the design of secure cyber physical systems. In: Proceedings of the 3rd International Workshop on Cyber-Physical Systems for Smart Water Networks. pp. 25–28.

2. The role of machine learning in cybersecurity;Apruzzese;Digit. Threats: Res. Pract.,2023

3. Arp, D., Quiring, E., Pendlebury, F., Warnecke, A., Pierazzi, F., Wressnegger, C., Cavallaro, L., Rieck, K., 2022. Dos and don’ts of machine learning in computer security. In: 31st USENIX Security Symposium (USENIX Security 22). pp. 3971–3988.

4. USAD: UnSupervised anomaly detection on multivariate time series;Audibert,2020

5. LOF: identifying density-based local outliers;Breunig,2000

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