Evaluating the Impact of Privacy-Preserving Federated Learning on CAN Intrusion Detection

TL;DR

This paper assesses how privacy-preserving federated learning impacts CAN intrusion detection, focusing on detection accuracy and communication costs, and compares it to traditional centralized methods.

Contribution

It introduces a federated learning approach for CAN intrusion detection using LSTM autoencoders and evaluates its effectiveness and efficiency.

Findings

Federated learning achieves comparable detection accuracy to centralized methods.

FL reduces communication overhead in vehicular intrusion detection.

The proposed FL system is feasible for real-world deployment.

Abstract

The challenges derived from the data-intensive nature of machine learning in conjunction with technologies that enable novel paradigms such as V2X and the potential offered by 5G communication, allow and justify the deployment of Federated Learning (FL) solutions in the vehicular intrusion detection domain. In this paper, we investigate the effects of integrating FL strategies into the machine learning-based intrusion detection process for on-board vehicular networks. Accordingly, we propose a FL implementation of a state-of-the-art Intrusion Detection System (IDS) for Controller Area Network (CAN), based on LSTM autoencoders. We thoroughly evaluate its detection efficiency and communication overhead, comparing it to a centralized version of the same algorithm, thereby presenting it as a feasible solution.