Give and Take: Federated Transfer Learning for Industrial IoT Network Intrusion Detection

TL;DR

This paper introduces a federated transfer learning approach with a combinational neural network for effective intrusion detection in Industrial IoT networks, enhancing privacy and outperforming existing machine learning methods.

Contribution

It presents a novel FTL framework with a specialized neural network for IIoT intrusion detection, demonstrating improved accuracy and privacy preservation over traditional methods.

Findings

High detection performance across iterations

Better overall accuracy than existing algorithms

Effective privacy-preserving model training

Abstract

The rapid growth in Internet of Things (IoT) technology has become an integral part of today's industries forming the Industrial IoT (IIoT) initiative, where industries are leveraging IoT to improve communication and connectivity via emerging solutions like data analytics and cloud computing. Unfortunately, the rapid use of IoT has made it an attractive target for cybercriminals. Therefore, protecting these systems is of utmost importance. In this paper, we propose a federated transfer learning (FTL) approach to perform IIoT network intrusion detection. As part of the research, we also propose a combinational neural network as the centerpiece for performing FTL. The proposed technique splits IoT data between the client and server devices to generate corresponding models, and the weights of the client models are combined to update the server model. Results showcase high performance for the FTL setup between iterations on both the IIoT clients and the server. Additionally, the proposed FTL setup achieves better overall performance than contemporary machine learning algorithms at performing network intrusion detection.