Lightweight federated learning-based intrusion detection system for industrial internet of things

Abstract

As machine learning technology advances, data security becomes increasingly important. In this study, we propose an intrusion detection mechanism based on federated learning (FL) that updates only the learning weights to minimize the risk of information leakage. Considering the limited resources of industrial Internet of Things (IIoT) nodes, we propose a learning method based on data pruning. The proposed FL-based intrusion detection model was found to be more secure than the centralized model in terms of the data leakage rate. Data pruning technology reduced the memory usage by 1.4 times while maintaining 97.7 % accuracy. The proposed method detects attacks in industrial sites where large-scale IIoT nodes are installed efficiently, and protects industrial secrets and personal information effectively.