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

Eunbi Seo, Hyun Min Song, Huy Kang Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2018 16th Annual Conference on Privacy, Security and Trust, PST 2018
EditorsRobert H. Deng, Stephen Marsh, Jason Nurse, Rongxing Lu, Sakir Sezer, Paul Miller, Liqun Chen, Kieran McLaughlin, Ali Ghorbani
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538674932
DOIs
Publication statusPublished - 2018 Oct 29
Event16th Annual Conference on Privacy, Security and Trust, PST 2018 - Belfast, Northern Ireland, United Kingdom
Duration: 2018 Aug 282018 Aug 30

Publication series

Name2018 16th Annual Conference on Privacy, Security and Trust, PST 2018

Conference

Conference16th Annual Conference on Privacy, Security and Trust, PST 2018
CountryUnited Kingdom
CityBelfast, Northern Ireland
Period18/8/2818/8/30

Keywords

  • Controller Area Network
  • generative Adversarial Nets
  • in-vehicle security
  • intrusion detection System

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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  • Cite this

    Seo, E., Song, H. M., & Kim, H. K. (2018). GIDS: GAN based Intrusion Detection System for In-Vehicle Network. In R. H. Deng, S. Marsh, J. Nurse, R. Lu, S. Sezer, P. Miller, L. Chen, K. McLaughlin, & A. Ghorbani (Eds.), 2018 16th Annual Conference on Privacy, Security and Trust, PST 2018 [8514157] (2018 16th Annual Conference on Privacy, Security and Trust, PST 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PST.2018.8514157