GIDS: GAN based Intrusion Detection System for In-Vehicle Network (1907.07377v1)

Abstract: 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.

An Expert Overview of the GIDS Paper: A GAN-Based Intrusion Detection System for In-Vehicle Networks

The paper presents a novel approach to intrusion detection within in-vehicle networks through the use of Generative Adversarial Networks (GANs), articulated as the GAN-based Intrusion Detection System (GIDS). This paper is motivated by the increasing demand for safeguarding in-vehicle networks, primarily due to the inherent vulnerabilities of the Controller Area Network (CAN) bus. CAN's lack of built-in security features makes it a target for cyber-attacks, with potential consequences that extend beyond vehicular malfunction to threaten driver safety.

Background and Motivation

The demand for robust intrusion detection systems (IDS) for vehicular networks has become more critical due to the rise of sophisticated cyber threats. Traditional IDS, while effective in certain contexts, often possess limitations in adaptability, complexity, and the specificity of attack detection. Notably, they may fail to generalize to new, unidentified threats or require extensive retraining in new environments. The paper addresses these limitations by leveraging GANs to create a system that can detect not only known but also unknown forms of network intrusions, thereby enhancing both security and efficacy.

Methodology

The authors propose the GIDS model, which distinguishes itself through three primary characteristics: expandability, effectiveness, and security, achieved via a deep learning framework typical of GANs. The model employs a two-pronged discriminator approach designed to identify known and unknown attack types:

1. Known Attack Detection: The first discriminator is trained using real attack data, allowing it to identify specific, previously observed attack vectors within the vehicle's network stream.
2. Unknown Attack Detection: By using the GAN framework, the second discriminator is trained alongside a generator that produces synthetic 'fake' data. This adversarial process enables the discriminator to accurately differentiate these from normal data, thereby being equipped to flag atypical patterns arising from unknown intrusions.

A notable innovation in this methodology is the transformation of CAN data into image-like structures dubbed 'CAN images' through one-hot-vector encoding. This makes the data more amenable to processing by neural networks, thereby increasing detection speed and accuracy.

Results

The GIDS model demonstrates high accuracy in intrusion detection across a suite of attacks, maintaining a general detection rate of 98% for previously unknown attacks. The integration of both discriminators further improves performance, with the first discriminator achieving approximately 100% accuracy for known of attacks. These results indicate a promising ability to address the limitations of traditional IDS in vehicular contexts.

Implications and Future Directions

The efficacy of GIDS, as demonstrated in the experiments with Hyundai's YF Sonata, suggests that it is robust enough to be adapted across diverse vehicular architectures, providing a more universal application potential. The use of deep learning models in IDS indicates an ongoing shift toward more autonomous and intelligent network security mechanisms.

Despite the promising results presented, the paper recognizes challenges in differentiating anomalous data patterns caused by genuine component malfunctions from those resulting from cyber-attacks. Further research will be needed to refine these distinctions and to evaluate the system's application across a broader range of vehicular systems and environments.

In conclusion, the GIDS model introduces an advanced intrusion detection methodology that extends current capabilities by learning both known and unknown attack patterns within vehicular contexts. This innovation paves the way for further exploration of GANs within the domain of automotive security, signaling a potential pivot towards comprehensive, adaptable defense mechanisms against emerging vehicular threats.