A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network

TL;DR

This survey reviews learning-based in-vehicle intrusion detection systems for connected vehicles, focusing on machine learning, deep learning, and federated learning approaches, highlighting their capabilities, limitations, and future research directions.

Contribution

It provides a comprehensive categorization and critical analysis of existing IDS methods, emphasizing attack detection, evaluation metrics, and the potential of federated learning in CAV security.

Findings

ML and DL approaches effectively detect known attacks

Federated Learning offers privacy-preserving security solutions

Current methods face limitations in detecting unknown attacks

Abstract

Connected and Autonomous Vehicles (CAVs) enhance mobility but face cybersecurity threats, particularly through the insecure Controller Area Network (CAN) bus. Cyberattacks can have devastating consequences in connected vehicles, including the loss of control over critical systems, necessitating robust security solutions. In-vehicle Intrusion Detection Systems (IDSs) offer a promising approach by detecting malicious activities in real time. This survey provides a comprehensive review of state-of-the-art research on learning-based in-vehicle IDSs, focusing on Machine Learning (ML), Deep Learning (DL), and Federated Learning (FL) approaches. Based on the reviewed studies, we critically examine existing IDS approaches, categorising them by the types of attacks they detect - known, unknown, and combined known-unknown attacks - while identifying their limitations. We also review the evaluation metrics used in research, emphasising the need to consider multiple criteria to meet the requirements of safety-critical systems. Additionally, we analyse FL-based IDSs and highlight their limitations. By doing so, this survey helps identify effective security measures, address existing limitations, and guide future research toward more resilient and adaptive protection mechanisms, ensuring the safety and reliability of CAVs.