Enhancing Security in Vehicular Networks: A Study of Controller Area Network Intrusion Detection Systems

• Version
• Download 27
• File Size 0.00 KB
• File Count 1
• Create Date 13 December, 2025
• Last Updated 13 December, 2025

Enhancing Security in Vehicular Networks: A Study of Controller Area Network Intrusion Detection Systems

DOI : 10.46335/IJIES.2024.9.2.17

Authors : Vishal R. Deshmukh, Prof. Dr. Indrabhan S. Borse

Abstract – The increasing integration of advanced technologies in vehicular systems has led to a rising concern regarding the security of Controller Area Network (CAN) communication within vehicles. As the backbone of in-vehicle communication, the CAN bus is susceptible to various cyber threats and attacks. Intrusion Detection Systems (IDS) have emerged as a crucial defense mechanism to safeguard the integrity and functionality of vehicular networks. This survey paper provides a comprehensive analysis of the state-of-the-art in Controller Area Network Intrusion Detection Systems (CAN IDS). It explores various methodologies, techniques, and strategies employed to detect and mitigate potential intrusions in vehicular networks. The survey encompasses a wide spectrum of research and development in the field, encompassing anomaly detection, signature-based detection, machine learning, deep learning, and hybrid approaches tailored specifically for the CAN environment. Moreover, the survey investigates the challenges and limitations encountered in deploying IDS in vehicular networks, including real-time processing constraints, computational overhead, scalability, and the dynamic nature of vehicular communication.