Enhanced Anomaly Detection for Cyber-Attack Detection in SmartWater Distribution Systems
Publikation aus Digital, Policies
Forschungsgruppe Cyber Security and Defence
Graz ARES '22: Proceedings of the 17th International Conference on Availability, Reliability and Security, 8/2022
The importance of automated intrusion detection systems, not only in network infrastructures, but also in critical and industrial infrastructures is becoming more evident with the significant increase of cyber-attacks targeting such infrastructures. The most recent research initiatives in this field focus on unsupervised learning methods, due to a constant lack of labelled datasets of a good quality. This paper proposes an enhanced autoencoder based anomaly detection approach for water distribution cyber-attack detection. The proposed approach contains a pipeline of methods, including feature engineering as a pre-processing step, anomaly estimation based on autoencoder, and scores smoothing as a post-processing step. The obtained results are very promising compared to existing approaches.
Keywords: Anomaly Detection, Deep Learning, Autoencoder, Cyber-Physical, Systems, Critical Infrastructure, Smart Water Distribution, Cyber-Security