Enhanced Anomaly Detection for Cyber-Attack Detection in Smart Water Distribution Systems

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.