Semi-supervised deep learning-driven anomaly detection schemes for cyber-attack detection in smart grids

Modern power systems are continuously exposed to malicious cyber-attacks. Analyzing industrial control system (ICS) traffic data plays a central role in detecting and defending against cyber-attacks. Detection approaches based on system modeling require effectively modeling the complex behavior of the critical infrastructures, which remains a challenge, especially for large-scale systems. Alternatively, data-driven approaches which rely on data collected from the inspected system have become appealing due to the availability of big data that supports machine learning methods to achieve outstanding performance. This chapter presents an enhanced cyber-attack detection strategy using unlabeled…