Light-weight federated learning-based anomaly detection for time-series data in industrial control systems

Thu-Huong Truong, Phuong Bac Ta, Le Anh Quang, Nguyen Minh Dan, Le Thanh Cong, Nguyen Xuan Hoang, Do Thu Ha, Nguyen Tai Hung, Kim Phuc Tran

Abstract: Highlights•A fast-learning model in 20-min time scale that can cope with frequent updating.•A light-weight detection scheme in terms of CPU, Memory usage, and running time.•Faster system response upon attacks since detection is implemented near the sources.•An accurate anomaly detection scheme for time-series data.•Federated learning to reduce bandwidth consumption on the link from Edge to Cloud.