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 language | English |
---|---|
Title of host publication | 2018 16th Annual Conference on Privacy, Security and Trust, PST 2018 |
Editors | Robert H. Deng, Stephen Marsh, Jason Nurse, Rongxing Lu, Sakir Sezer, Paul Miller, Liqun Chen, Kieran McLaughlin, Ali Ghorbani |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781538674932 |
DOIs | |
Publication status | Published - 2018 Oct 29 |
Event | 16th Annual Conference on Privacy, Security and Trust, PST 2018 - Belfast, Northern Ireland, United Kingdom Duration: 2018 Aug 28 → 2018 Aug 30 |
Publication series
Name | 2018 16th Annual Conference on Privacy, Security and Trust, PST 2018 |
---|
Conference
Conference | 16th Annual Conference on Privacy, Security and Trust, PST 2018 |
---|---|
Country/Territory | United Kingdom |
City | Belfast, Northern Ireland |
Period | 18/8/28 → 18/8/30 |
Bibliographical note
Publisher Copyright:© 2018 IEEE.
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