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Cluster Computing
Published in: Cluster Computing 6/2019

02-04-2018

Intrusion detection system using SOEKS and deep learning for in-vehicle security

Authors: Lulu Gao, Fei Li, Xiang Xu, Yong Liu

Published in: Cluster Computing | Special Issue 6/2019

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Abstract

With the continuous development of the intelligent vehicle, vehicle security events occur frequently, therefore, the vehicle information security is particularly important. In this paper, the in-vehicle security measures are analyzed, especially the current situation of in-vehicle intrusion detection system, which are mainly aimed at specific vehicles and are not enough to meet the need of vehicle security. Then, a new in-vehicle intrusion detection mechanism is proposed based on deep learning and the set of experience knowledge structure (SOEKS), which is a knowledge representation structure. Utilizing SOEKS and information entropy to increase the versatility of intrusion detection for different vehicle. In practice, the more precise model for specific vehicle can formed by training a large amount of specific vehicle data through deep learning. It is demonstrated with experimental results that the proposed approach is able to have 98% accuracy and detect a wide range of in-vehicle attacks.
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Literature
1.
Koscher, K., Czeskis, A., Roesner, F., et al.: Experimental security analysis of a modern automobile. IEEE J. Sel. Top. Quantum Electron. 41(3), 447–462 (2010)
2.
Xiao-gang, L., Bin, Y.: Analysis on security defense problem of internet of vehicles. Mob. Commun. 39(11), 30–33 (2015)
3.
Cho, A., Jo, H.J., Woo, S., et al.: Message authentication and key distribution mechanism secure against CAN bus attack. J. Korea Inst. Inf. Secur. Cryptol. 22(5), 1057–1068 (2012)
4.
Groza, B., Murvay, S.: Efficient protocols for secure broadcast in controller area networks. IEEE Trans. Ind. Inf. 9(4), 2034–2042 (2013)CrossRef
5.
Woo, S., Jo, H.J., Dong, H.L.: A practical wireless attack on the connected car and security protocol for in-vehicle CAN. IEEE Trans. Intell. Transp. Syst. 16(2), 993–1006 (2015)
6.
Kleberger, P., Olovsson, T., Jonsson, E.: Security aspects of the in-vehicle network in the connected car. Intell. Veh. Symp. 30(1), 528–533 (2011)
7.
Kang, M.J., Kang, J.W.: Intrusion detection system using deep neural network for in-vehicle network security. PLoS ONE 11(6), e0155781 (2016)CrossRef
8.
Sanin, C.: Applying decisional DNA to Internet of things: the concept and initial case study. Cybern. Syst. 46(1–2), 84–93 (2015)
9.
Sanin, C., Toro, C., Haoxi, Z., et al.: Decisional DNA: a multi-technology shareable knowledge structure for decisional experience. Neurocomputing 88(7), 42–53 (2012)CrossRef
10.
Zhang, H., Saní, C.N., et al.: Implementing fuzzy logic to generate user profile in decisional dna television: the concept and initial case study. Cybern. Syst. 44(2–3), 275–283 (2013)CrossRef
11.
Längkvist, M., Karlsson, L., Loutfi, A.: A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recogn. Lett. 42(1), 11–24 (2014)CrossRef
12.
Lopes, N., Ribeiro, B.: Towards adaptive learning with improved convergence of deep belief networks on graphics processing units. Pattern Recogn. 47(1), 114–127 (2014)CrossRef
13.
Zhou, L., Pan, S., Wang, J., et al.: Machine learning on big data: opportunities and challenges. Neurocomputing 237, 350–361 (2017)CrossRef
14.
Shang, C., Yang, F., Huang, D., et al.: Data-driven soft sensor development based on deep learning technique. J. Process Control 24(3), 223–233 (2014)CrossRef
15.
Davis, R.I., Burns, A., Bril, R.J., et al.: Controller area network (CAN) schedulability analysis: refuted, revisited and revised. Real-Time Syst. 35(3), 239–272 (2007)CrossRef
16.
Shreejith, S., Fahmy, S.A., Lukasiewycz, M.: Reconfigurable computing in next-generation automotive networks. IEEE Embed. Syst. Lett. 5(1), 12–15 (2013)CrossRef
17.
Ruth, R., Bartlett, W., Daily, J.: Accuracy of event data in the 2010 and 2011 Toyota camry during steady state and braking conditions. SAE Int. J. Passeng. Cars Electron. Electr. Syst. 5(1), 358–372 (2012)CrossRef
18.
Natale, M.D., Zeng, H., Giusto, P., et al.: Understanding and using the controller area network communication protocol. Theory Pract. 26(4), 37–40 (2012)
19.
Tobias, H., Kiltz, S., Dittmann, J.: Applying intrusion detection to automotive IT-early insights and remaining challenges. J. Inf. Assur. Secur. (JIAS) 4, 226–235 (2009)
20.
Hoppe, T., Kiltz, S., Dittmann, J.: Security threats to automotive CAN networks—practical examples and selected short-term countermeasures. Reliab. Eng. Syst. Saf. 96(1), 11–25 (2011)CrossRef
21.
Yin, C.L., Zhu, Y.F., Fei, J.L., et al.: A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access 5, 21954–21961 (2017)CrossRef
22.
Wang, S.Z., Li, Y.Z.: Intrusion detection algorithm based on deep learning and semi-supervised learning. Inf. Technol. 1, 101–104,108 (2017)
23.
Mohammadi, S., Namadchian, A.: A new deep learning approach for anomaly base IDS using memetic classifier. Int. J. Comput. Commun. Control 12(5), 677–688 (2017)CrossRef
24.
Li, B.M., Xie, S.Q., Xu, X.: Recent development of knowledge-based systems, methods and tools for one-of-a-kind production. Knowl.-Based Syst. 24(7), 1108–1119 (2011)CrossRef
25.
Zhang, H., Li, F., Wang, J., et al.: Adding intelligence to cars using the neural knowledge DNA. Cybern. Syst. 48(3), 267–273 (2017)CrossRef
26.
Zhang, H., Sanin, C., Szczerbicki, E.: Towards neural knowledge DNA. J. Intell. Fuzzy Syst. 32(2), 1575–1584 (2017)CrossRef
27.
Bereziński, P., Jasiul, B., Szpyrka, M.: An entropy-based network anomaly detection method. Entropy 17(4), 2367–2408 (2015)CrossRef
28.
He, Yu., Gui-he, Q., et al.: Cyber security and anomaly detection method for in-vehicle CAN. J. Jilin Univ. 46(4), 1246–1253 (2016)
Metadata
Title
Intrusion detection system using SOEKS and deep learning for in-vehicle security
Authors
Lulu Gao
Fei Li
Xiang Xu
Yong Liu
Publication date
02-04-2018
Publisher
Springer US
Published in
Cluster Computing / Issue Special Issue 6/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-018-2385-7

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