Vehicular Intrusion Detection System for Controller Area Network: A Comprehensive Survey and Evaluation

TL;DR

This paper addresses the rising cybersecurity risk in modern vehicles, focusing on CAN-based in-vehicle networks and the need for effective Vehicular Intrusion Detection Systems (VIDS). It conducts a comprehensive survey and taxonomy of CAN attacks and VID defenses, reproduces several VID methods on a common dataset, and evaluates them to reveal gaps in attack coverage and practical deployment challenges. The authors analyze 34 attack studies and 53 VIDS, classifying attacks into 18 categories and providing a cross-method comparison across features, detection technologies, and validation platforms. They discuss key constraints, such as hardware limitations, private protocols, and the reliance on machine learning, and propose defense recommendations and future research directions for robust, deployable CAN security in intelligent and IoV-enabled vehicles.

Abstract

The progress of automotive technologies has made cybersecurity a crucial focus, leading to various cyber attacks. These attacks primarily target the Controller Area Network (CAN) and specialized Electronic Control Units (ECUs). In order to mitigate these attacks and bolster the security of vehicular systems, numerous defense solutions have been proposed.These solutions aim to detect diverse forms of vehicular attacks. However, the practical implementation of these solutions still presents certain limitations and challenges. In light of these circumstances, this paper undertakes a thorough examination of existing vehicular attacks and defense strategies employed against the CAN and ECUs. The objective is to provide valuable insights and inform the future design of Vehicular Intrusion Detection Systems (VIDS). The findings of our investigation reveal that the examined VIDS primarily concentrate on particular categories of attacks, neglecting the broader spectrum of potential threats. Moreover, we provide a comprehensive overview of the significant challenges encountered in implementing a robust and feasible VIDS. Additionally, we put forth several defense recommendations based on our study findings, aiming to inform and guide the future design of VIDS in the context of vehicular security.

Figures (6)

• Figure 1: Two common IVN architectures.
• Figure 2: The threat model of the attack.
• Figure 3: Detailed taxonomy of attack scenarios.
• Figure 4: Existing intrusion detection systems.
• Figure 5: The topology of the real CAN.