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http://dx.doi.org/10.7471/ikeee.2022.26.4.622

Intrusion Detection System for In-Vehicle Network to Improve Detection Performance Considering Attack Counts and Attack Types  

Hyunchul, Im (Soongsil University)
Donghyeon, Lee (Soongsil University)
Seongsoo, Lee (Soongsil University)
Publication Information
Journal of IKEEE / v.26, no.4, 2022 , pp. 622-627 More about this Journal
Abstract
This paper proposes an intrusion detection system for in-vehicle network to improve detection performance considering attack counts and attack types. In intrusion detection system, both FNR (False Negative Rate), where intrusion frame is misjudged as normal frame, and FPR (False Positive Rate), where normal frame is misjudged as intrusion frame, seriously affect vechicle safety. This paper proposes a novel intrusion detection algorithm to improve both FNR and FPR, where data frame previously detected as intrusion above certain attack counts is automatically detected as intrusion and the automatic intrusion detection method is adaptively applied according to attack types. From the simulation results, the propsoed method effectively improve both FNR and FPR in DoS(Denial of Service) attack and spoofing attack.
Keywords
Controller Area Network; Intrusion Detection System; Random Forest; Machine Learning; In-Vehicle Network; Attack Count; Attack Type;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 T. Hoppe, S. Kiltz, and J. Dittmann, "Security threats to automotive CAN networks - Practical examples and selected short-term countermeasures," Reliability Engineering & System Safety, vol.96, no.1, pp.11-25, 2011. DOI: 10.1016/j.ress.2010.06.026   DOI
2 E. Aliwa, C. Perera, and O. Rana, "Cyberattacks and Countermeasures For In-Vehicle Networks," ACM Computing Surveys, vol.54, no.1, pp.1-37, 2020. DOI: 10.1145/3431233   DOI
3 A. Theissler, "Anomaly detection in recordings from in-vehicle networks," Proceedings of International Workshop on Big Data Applications and Principles, pp.1-10, 2014.
4 A. Tomlinson, J. Bryans, and S. Shaikh, "Using a one-class compound classifier to detect in-vehicle network attacks," Proceedings of Genetic and Evolutionary Computation Conference, pp.1926-1929, 2018. DOI: 10.1145/3205651.3208223   DOI
5 D. Tian, Y. Li, Y. Wang, X. Duan, C. Wang, W. Wang, R. Hui, and P. Guo, "An intrusion detection system based on machine learning for CAN-Bus," Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, vol.221, pp.285-294, 2018. DOI: 10.1007/978-3-319-74176-5_25   DOI
6 M. Kang and J. Kang, "Intrusion detection system using deep neural network for in-vehicle network security," PLoS ONE, vol.11, no.6, pp.1-17, 2016. DOI: 10.1371/journal.pone.0155781   DOI
7 E. Seo, H. Song, and H. Kim, "GIDS: GAN-Based Intrusion Detection System for In-Vehicle Network," Proceedings of Annual Conference on Privacy, Security and Trust, pp.1-6, 2018. DOI: 10.1109/PST.2018.8514157   DOI
8 H. Song, J. Woo, and H. Kim, "In-vehicle network intrusion detection using deep convolutional neural network," Vehicular Communications, vol.21, pp.100198, 2020. DOI: 10.1016/j.vehcom.2019.100198   DOI
9 T. Kang, J. Lee, and S. Lee, "Counterattack Method against Hacked Node in CAN Bus Physical Layer," j.inst.Korean.electr.electron.eng., vol.23, no.4, pp.1469-1472, 2019. DOI: 10.7471/ikeee.2019.23.4.1469   DOI
10 H. Song, H. Kim, and H. Kim, "Intrusion detection system based on the analysis of time intervals of CAN messages for in-vehicle network," Proceedings of International Conference on Information Networking, pp.63-68, 2016. DOI: 10.1109/ICOIN.2016.7427089   DOI
11 D. Stabil, M. Marchetti, and M. Colajanni, "Detecting Attacks to Internal Vehicle Networks through Hamming Distance," Proceedings of AEIT International Annual Conference, pp.1-6, 2017. DOI: 10.23919/AEIT.2017.8240550   DOI
12 A. Tomlinson, J. Bryans, and S. Shaikh, "Towards Viable Intrusion Detection Methods for The Automotive Controller Area Network," Proceedings of Computer Science in Cars Symposium, pp.1-9, 2018. DOI: 10.1145/3273946.3273950   DOI