GIDS: GAN based Intrusion Detection System for In-Vehicle Network
This addresses the critical need for accurate intrusion detection in vehicle networks to ensure driver safety, though it is an incremental application of existing GAN methods to a new domain.
The paper tackles the problem of detecting unknown attacks in in-vehicle networks, where traditional intrusion detection systems are insufficient due to a lack of known attack signatures, and proposes GIDS, a GAN-based system that achieves high detection accuracy for four unknown attacks using only normal data.
A Controller Area Network (CAN) bus in the vehicles is an efficient standard bus enabling communication between all Electronic Control Units (ECU). However, CAN bus is not enough to protect itself because of lack of security features. To detect suspicious network connections effectively, the intrusion detection system (IDS) is strongly required. Unlike the traditional IDS for Internet, there are small number of known attack signatures for vehicle networks. Also, IDS for vehicle requires high accuracy because any false-positive error can seriously affect the safety of the driver. To solve this problem, we propose a novel IDS model for in-vehicle networks, GIDS (GAN based Intrusion Detection System) using deep-learning model, Generative Adversarial Nets. GIDS can learn to detect unknown attacks using only normal data. As experiment result, GIDS shows high detection accuracy for four unknown attacks.