CAN INTRUSION DETECTION SYSTEM USING GAN

Abstract

Throughout the history of automobiles, advancements have been made to improve the safety and comfort of driving. One of the latest developments involves replacing the wiring between electronic control units (ECUs) with a networking standard called a Controller Area Network (CAN). While CAN has proven to be an effective communication protocol, it lacks security features that could not prevent malicious activities on the network. Therefore, there is a need for an Intrusion detection system (IDS) that can monitor CAN network traffic and identify any suspicious behavior. This work proposes an IDS for in-vehicle network communication that can detect both known and unknown malicious activities using deep learning techniques. The proposed IDS is based on Generative Adversarial Networks (GANs) which offers several novel features compared to traditional IDS techniques. The proposed IDS GAN model is evaluated using the Real ORNL Automotive Dynamometer (ROAD) CAN Intrusion Dataset, which contains many network traffic samples. The results shows that the model achieves high accuracy of 99%. Also had done a comparison with different enhanced CNN models to detect known attacks. It is evident from the above experiments that the model based on GANs can effectively detect network attacks and has the potential to be applied in real-world scenarios to enhance network security.