Research Area: Machine Learning
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, 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.
Keywords:
Light-Weight
Federated Learning
Anomaly Detection
Time-Series Data
Industrial Control Systems
Deep Learning
Machine Learning
Author(s) Name: Huong Thu Truong, Bac Phuong Ta, Quang Anh Le, Dan Minh Nguyen, Cong Thanh Le, Hoang Xuan Nguyen, Ha Thu Do, Hung Tai Nguyen, Kim Phuc Tran
Journal name: Computers in Industry
Conferrence name:
Publisher name: ELSEVIER
DOI: 10.1016/j.compind.2022.103692
Volume Information: Volume 140, September 2022, 103692
