The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
GIDS: GAN based Intrusion Detection System for In-Vehicle Network
Open accessAbstract
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.
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Cites
Labaca-Castro, R. (2023) · Machine Learning under Malware Attack · doi:10.1007/978-3-658-40442-0_9 · Cited by 19,272
GAN（Generative Adversarial Nets）
(2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,270
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., & Langs, G. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-59050-9_12 · Cited by 2,627
Song, H. M., Kim, H. R., & Kim, H. K. (2016) · 2016 International Conference on Information Networking (ICOIN) · doi:10.1109/icoin.2016.7427089 · Cited by 447
Self-citationOTIDS: A Novel Intrusion Detection System for In-vehicle Network by Using Remote Frame
Lee, H., Jeong, S. H., & Kim, H. K. (2017) · 2017 15th Annual Conference on Privacy, Security and Trust (PST) · doi:10.1109/pst.2017.00017 · Cited by 386
Self-citationEntropy-based anomaly detection for in-vehicle networks
Muter, M., & Asaj, N. (2011) · 2011 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2011.5940552 · Cited by 378
Hoppe, T., Kiltz, S., & Dittmann, J. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-87698-4_21 · Cited by 272
Anomaly detection of CAN bus messages through analysis of ID sequences
Marchetti, M., & Stabili, D. (2017) · 2017 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2017.7995934 · Cited by 239
Cited by
In-vehicle network intrusion detection using deep convolutional neural network
Song, H. M., Woo, J., & Kim, H. K. (2020) · Vehicular Communications · doi:10.1016/j.vehcom.2019.100198 · Cited by 586
Self-citationGenerative Adversarial Networks: A Survey Toward Private and Secure Applications
Cai, Z., Xiong, Z., Xu, H., Wang, P., Li, W., & Pan, Y. (2022) · ACM Computing Surveys · doi:10.1145/3459992 · Cited by 376
Ashraf, J., Bakhshi, A. D., Moustafa, N., Khurshid, H., Javed, A., & Beheshti, A. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3017882 · Cited by 295
Nwakanma, C. I., Ahakonye, L. A. C., Njoku, J. N., Odirichukwu, J. C., Okolie, S. A., Uzondu, C., Ndubuisi Nweke, C. C., & Kim, D.-S. (2023) · Applied Sciences · doi:10.3390/app13031252 · Cited by 202
Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks
Mehedi, S. T., Anwar, A., Rahman, Z., & Ahmed, K. (2021) · Sensors · doi:10.3390/s21144736 · Cited by 150
Metadata
- Authors
- Seo, E., Song, H. M., & Kim, H. K.
- Venue
- 2018 16th Annual Conference on Privacy, Security and Trust (PST)
- Publisher
- IEEE
- Published
- 2018-08
- Pages
- 1-6
- OpenAlex
- W2959120033
- Cited by
- 533
- References
- 8
- FWCI
- 21.30
- Type
- conference paper
- Language
- en
- Open access
- green
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- Seo, E., Song, H. M., & Kim, H. K. (2018). GIDS: GAN based Intrusion Detection System for In-Vehicle Network. 2018 16th Annual Conference on Privacy, Security and Trust (PST). https://doi.org/10.1109/pst.2018.8514157
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