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

  • Lulu Gao
  • Fei Li
  • Xiang Xu
  • Yong Liu


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.


In-vehicle security Intrusion detection system SOEKS Deep learning 


  1. 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)Google Scholar
  2. 2.
    Xiao-gang, L., Bin, Y.: Analysis on security defense problem of internet of vehicles. Mob. Commun. 39(11), 30–33 (2015)Google Scholar
  3. 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)Google Scholar
  4. 4.
    Groza, B., Murvay, S.: Efficient protocols for secure broadcast in controller area networks. IEEE Trans. Ind. Inf. 9(4), 2034–2042 (2013)CrossRefGoogle Scholar
  5. 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)Google Scholar
  6. 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)Google Scholar
  7. 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)CrossRefGoogle Scholar
  8. 8.
    Sanin, C.: Applying decisional DNA to Internet of things: the concept and initial case study. Cybern. Syst. 46(1–2), 84–93 (2015)Google Scholar
  9. 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)CrossRefGoogle Scholar
  10. 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)CrossRefGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. 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)CrossRefGoogle Scholar
  13. 13.
    Zhou, L., Pan, S., Wang, J., et al.: Machine learning on big data: opportunities and challenges. Neurocomputing 237, 350–361 (2017)CrossRefGoogle Scholar
  14. 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)CrossRefGoogle Scholar
  15. 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)CrossRefGoogle Scholar
  16. 16.
    Shreejith, S., Fahmy, S.A., Lukasiewycz, M.: Reconfigurable computing in next-generation automotive networks. IEEE Embed. Syst. Lett. 5(1), 12–15 (2013)CrossRefGoogle Scholar
  17. 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)CrossRefGoogle Scholar
  18. 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)Google Scholar
  19. 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)Google Scholar
  20. 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)CrossRefGoogle Scholar
  21. 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)CrossRefGoogle Scholar
  22. 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)Google Scholar
  23. 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)CrossRefGoogle Scholar
  24. 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)CrossRefGoogle Scholar
  25. 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)CrossRefGoogle Scholar
  26. 26.
    Zhang, H., Sanin, C., Szczerbicki, E.: Towards neural knowledge DNA. J. Intell. Fuzzy Syst. 32(2), 1575–1584 (2017)CrossRefGoogle Scholar
  27. 27.
    Bereziński, P., Jasiul, B., Szpyrka, M.: An entropy-based network anomaly detection method. Entropy 17(4), 2367–2408 (2015)CrossRefGoogle Scholar
  28. 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)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of CybersecurityChengdu University of Information TechnologyChengduChina

Personalised recommendations