Work

GIDS: GAN based Intrusion Detection System for In-Vehicle Network

Year: 2018

Type: conference-paper

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.

Source: Conference on Privacy, Security and Trust

DOI: 10.1109/pst.2018.8514157

Authors: Eunbi Seo, Hyun Min Song, Huy Kang Kim

Institution: Korea University

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funders: Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion