查看文章

Protecting Cyber-physical Systems (CPSs) is highly important for preserving sensitive information and detecting cyber threats. Developing a robust privacy-preserving anomaly detection method requires physical and network data about the systems, such as Supervisory Control and Data Acquisition (SCADA), for protecting original data and recognising cyber-attacks. In this paper, a new privacy-preserving anomaly detection framework, so-called PPAD-CPS, is proposed for protecting confidential information and discovering malicious observations in power systems and their network traffic. The framework involves two main modules. First, a data pre-processing module is suggested for filtering and transforming original data into a new format that achieves the target of privacy preservation. Second, an anomaly detection module is suggested using a Gaussian Mixture Model (GMM) and Kalman Filter (KF) for precisely …