Engineer Thu Ha Do Thu Ha Do is a researcher at International Research Institute for Artificial Intelligence and Data Science (IAD), Dong A University. She graduated from Ha Noi University of Science and Technology, majoring in  Electronics and Telecommunications. She has published two research papers. Her researches focus on AI, Machine learning applications for 360 Image Quality Assessment (IQA), and Anomaly Detection.

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

Abstract: With the emergence of the Industrial Internet of Things (IIoT), potential threats to smart manufacturing systems are increasingly becoming challenging, causing severe damage to production operations and vital industrial assets, even sensitive information. Hence, detecting irregularities for time-series data in industrial control systems that should operate continually is critical, ensuring security and minimizing maintenance costs. In this study, with the hybrid design of Federated learning, Autoencoder, Transformer, and Fourier mixing sublayer, we propose a robust distributed anomaly detection architecture that works more accurately than several most recent anomaly detection solutions within the ICS contexts, whilst being fast learning in minute time scale. This distributed architecture is also proven to achieve lightweight, consume little CPU and memory usage, and have low communication costs in terms of bandwidth consumption, which makes it feasible to be deployed on top of edge devices with limited computing capacity.