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 | United Kingdom |
| City | Belfast, Northern Ireland |
| Period | 18/8/28 → 18/8/30 |
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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
Cite this
GIDS : GAN based Intrusion Detection System for In-Vehicle Network. / Seo, Eunbi; Song, Hyun Min; Kim, Huy Kang.
2018 16th Annual Conference on Privacy, Security and Trust, PST 2018. ed. / Robert H. Deng; Stephen Marsh; Jason Nurse; Rongxing Lu; Sakir Sezer; Paul Miller; Liqun Chen; Kieran McLaughlin; Ali Ghorbani. Institute of Electrical and Electronics Engineers Inc., 2018. 8514157 (2018 16th Annual Conference on Privacy, Security and Trust, PST 2018).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - GIDS
T2 - GAN based Intrusion Detection System for In-Vehicle Network
AU - Seo, Eunbi
AU - Song, Hyun Min
AU - Kim, Huy Kang
PY - 2018/10/29
Y1 - 2018/10/29
N2 - 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.
AB - 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.
KW - Controller Area Network
KW - generative Adversarial Nets
KW - in-vehicle security
KW - intrusion detection System
UR - http://www.scopus.com/inward/record.url?scp=85063514589&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063514589&partnerID=8YFLogxK
U2 - 10.1109/PST.2018.8514157
DO - 10.1109/PST.2018.8514157
M3 - Conference contribution
AN - SCOPUS:85063514589
T3 - 2018 16th Annual Conference on Privacy, Security and Trust, PST 2018
BT - 2018 16th Annual Conference on Privacy, Security and Trust, PST 2018
A2 - Deng, Robert H.
A2 - Marsh, Stephen
A2 - Nurse, Jason
A2 - Lu, Rongxing
A2 - Sezer, Sakir
A2 - Miller, Paul
A2 - Chen, Liqun
A2 - McLaughlin, Kieran
A2 - Ghorbani, Ali
PB - Institute of Electrical and Electronics Engineers Inc.
ER -