Intrusion Detection in Internet of Vehicles Using Machine Learning
This addresses security vulnerabilities in connected vehicles, but it appears incremental as it applies existing machine learning methods to a new dataset without claiming major breakthroughs.
This paper tackles the problem of cyber-attacks like DoS and spoofing in the Internet of Vehicles by developing a machine learning-based intrusion detection system for CAN bus traffic, using the CiCIoV2024 dataset to analyze attack patterns and confirm structural differences between attack types and benign data.
The Internet of Vehicles (IoV) has evolved modern transportation through enhanced connectivity and intelligent systems. However, this increased connectivity introduces critical vulnerabilities, making vehicles susceptible to cyber-attacks such Denial-ofService (DoS) and message spoofing. This project aims to develop a machine learning-based intrusion detection system to classify malicious Controller Area network (CAN) bus traffic using the CiCIoV2024 benchmark dataset. We analyzed various attack patterns including DoS and spoofing attacks targeting critical vehicle parameters such as Spoofing-GAS - gas pedal position, Spoofing-RPM, Spoofing-Speed, and Spoofing-Steering\_Wheel. Our initial findings confirm a multi-class classification problem with a clear structural difference between attack types and benign data, providing a strong foundation for machine learning models.