@inproceedings{5f4ab559e4fb4d23a44bf94d37313b7a,
title = "GIDS: GAN based Intrusion Detection System for In-Vehicle Network",
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.",
keywords = "Controller Area Network, generative Adversarial Nets, in-vehicle security, intrusion detection System",
author = "Eunbi Seo and Song, {Hyun Min} and Kim, {Huy Kang}",
year = "2018",
month = oct,
day = "29",
doi = "10.1109/PST.2018.8514157",
language = "English",
series = "2018 16th Annual Conference on Privacy, Security and Trust, PST 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Deng, {Robert H.} and Stephen Marsh and Jason Nurse and Rongxing Lu and Sakir Sezer and Paul Miller and Liqun Chen and Kieran McLaughlin and Ali Ghorbani",
booktitle = "2018 16th Annual Conference on Privacy, Security and Trust, PST 2018",
note = "16th Annual Conference on Privacy, Security and Trust, PST 2018 ; Conference date: 28-08-2018 Through 30-08-2018",
}