Abstract
VANETs is a major enabling technology for connected and autonomous vehicles. Vehicles communicate wirelessly with other vehicles, sensors, humans, and infrastructure, thereby improving decision making based on the information received from its surroundings. However, for these applications to work correctly, information needs to be authenticated, verified and trustworthy. The most important messages in these networks are safety messages which are periodically broadcasted for various safety and traffic efficiency related applications such as collision avoidance, intersection warning, and traffic jam detection. However, the primary concern is guaranteeing the trustworthiness of the data in the presence of dishonest and misbehaving peers. Misbehavior detection is still in their infancy and requires a lot of effort to be integrated into the system. An attacker who is imitating “ghost vehicles” on the road, by broadcasting false position information in the safety messages, must be detected and revoked permanently from the VANETs. The goal of our work is analyzing safety messages and detecting false position information transmitted by the misbehaving nodes. In this paper, we use machine learning (ML) techniques on VeReMi dataset to detect the misbehavior. We demonstrated that the ML-based approach enables high-quality detection of modeled attack patterns. We believe that the ML-based approach is a feasible and effective way of detecting such misbehavior in a real-world scenario of VANETs.
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References
Santa, J., Pereñíguez, F., Moragón, A., Skarmeta, A.F.: Experimental evaluation of CAM and DENM messaging services in vehicular communications. Transp. Res. Part C: Emerg. Technol. 46, 98–120 (2014)
Kerrache, C.A., Calafate, C.T., Cano, J.C., Lagraa, N., Manzoni, P.: Trust management for vehicular networks: an adversary-oriented overview. IEEE Access 4, 9293–9307 (2016)
Hasrouny, H., Samhat, A.E., Bassil, C., Laouiti, A.: VANet security challenges and solutions: a survey. Veh. Commun. 7, 7–20 (2017)
Brecht, B., et al.: A security credential management system for V2X communications. IEEE Trans. Intell. Transp. Syst. (99), 1–22 (2018)
IEEE: IEEE Standard for Wireless Access in Vehicular Environments–Security Services for Applications and Management Messages. IEEE Std 1609.2-2016 (Revision of IEEE Std 1609.2-2013), pp. 1–240, March 2016
ETSI, T.: 102 940: Intelligent Transport Systems (ITS). Security; ITS communications security architecture and security management. Technical specification, European Telecommunications Standards Institute (2012)
Lu, Z., Qu, G., Liu, Z.: A survey on recent advances in vehicular network security, trust, and privacy. IEEE Trans. Intell. Transp. Syst. (2018)
Soleymani, S.A., et al.: Trust management in vehicular ad hoc network: a systematic review. EURASIP J. Wirel. Commun. Netw. 2015(1), 146 (2015)
Van der Heijden, R.W., Lukaseder, T., Kargl, F.: VeReMi: a dataset for comparable evaluation of misbehavior detection in VANETs. arXiv preprint arXiv:1804.06701 (2018)
Van der Heijden, R.W., Dietzel, S., Leinmüller, T., Kargl, F.: Survey on misbehavior detection in cooperative intelligent transportation systems. arXiv preprint arXiv:1610.06810 (2016)
Khan, U., Agrawal, S., Silakari, S.: A detailed survey on misbehavior node detection techniques in vehicular ad hoc networks. In: Mandal, J.K., Satapathy, S.C., Sanyal, M.K., Sarkar, P.P., Mukhopadhyay, A. (eds.) Information Systems Design and Intelligent Applications. AISC, vol. 339, pp. 11–19. Springer, New Delhi (2015). https://doi.org/10.1007/978-81-322-2250-7_2
Zhang, J.: A survey on trust management for VANETs. In: International Conference on Advanced Information Networking and Applications (AINA), pp. 105–112. IEEE (2011)
Ma, S., Wolfson, O., Lin, J.: A survey on trust management for Intelligent Transportation System. In: Proceedings of the 4th ACM SIGSPATIAL International Workshop on Computational Transportation Science, pp. 18–23. ACM (2011)
Rawat, D.B., Bista, B.B., Yan, G., Weigle, M.C.: Securing vehicular ad-hoc networks against malicious drivers: a probabilistic approach. In: 2011 International Conference on Complex, Intelligent and Software Intensive Systems (CISIS), pp. 146–151. IEEE (2011)
Hsiao, H.C., Studer, A., Dubey, R., Shi, E., Perrig, A.: Efficient and secure threshold-based event validation for VANETs. In: Proceedings of the Fourth ACM Conference on Wireless Network Security, pp. 163–174. ACM (2011)
Zhuo, X., Hao, J., Liu, D., Dai, Y.: Removal of misbehaving insiders in anonymous VANETs. In: Proceedings of the 12th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pp. 106–115. ACM (2009)
Leinmüller, T., Schmidt, R.K., Held, A.: Cooperative position verification-defending against roadside attackers 2.0. In: Proceedings of 17th ITS World Congress, pp. 1–8 (2010)
Bilogrevic, I., Manshaei, M.H., Raya, M., Hubaux, J.P.: Optimal revocations in ephemeral networks: a game-theoretic framework. In: 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt), pp. 21–30. IEEE (2010)
Stubing, H., Jaeger, A., Schmidt, C., Huss, S.A.: Verifying mobility data under privacy considerations in Car-to-X communication. In: 17th ITS World CongressITS JapanITS AmericaERTICO (2010)
Stübing, H., Firl, J., Huss, S.A.: A two-stage verification process for Car-to-X mobility data based on path prediction and probabilistic maneuver recognition. In: 2011 IEEE Vehicular Networking Conference (VNC), pp. 17–24. IEEE (2011)
Yang, Z., Yang, K., Lei, L., Zheng, K., Leung, V.C.: Blockchain-based decentralized trust management in vehicular networks. IEEE Internet of Things J. (2018)
Grover, J., Prajapati, N.K., Laxmi, V., Gaur, M.S.: Machine learning approach for multiple misbehavior detection in VANET. In: Abraham, A., Mauri, J.L., Buford, J.F., Suzuki, J., Thampi, S.M. (eds.) ACC 2011. CCIS, vol. 192, pp. 644–653. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22720-2_68
Kang, M.J., Kang, J.W.: Intrusion detection system using deep neural network for in-vehicle network security. PloS One 11(6), e0155781 (2016)
Loukas, G., Vuong, T., Heartfield, R., Sakellari, G., Yoon, Y., Gan, D.: Cloud-based cyber-physical intrusion detection for vehicles using Deep Learning. IEEE Access 6, 3491–3508 (2018)
Taylor, A., Leblanc, S., Japkowicz, N.: Anomaly detection in automobile control network data with long short-term memory networks. In: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 130–139. IEEE (2016)
Ali Alheeti, K.M., Gruebler, A., McDonald-Maier, K.: Intelligent intrusion detection of grey hole and rushing attacks in self-driving vehicular networks. Computers 5(3), 16 (2016)
IEEE Std.: IEEE Standard for Information technology – Local and metropolitan area networks – Specific requirements – Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications Amendment 6: Wireless Access in Vehicular Environments, pp. 1–51, July 2010
Codeca, L., Frank, R., Faye, S., Engel, T.: Luxembourg SUMO traffic (LuST) scenario: traffic demand evaluation. IEEE Intell. Transp. Syst. Mag. 9(2), 52–63 (2017)
Acknowledgments
The research work has been conducted in the Information Security Education and Awareness (ISEA) Lab of Indian Institute of Technology Guwahati. The authors would like to acknowledge IIT Guwahati and ISEA MeitY, India for the support.
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Singh, P.K., Gupta, S., Vashistha, R., Nandi, S.K., Nandi, S. (2019). Machine Learning Based Approach to Detect Position Falsification Attack in VANETs. In: Nandi, S., Jinwala, D., Singh, V., Laxmi, V., Gaur, M., Faruki, P. (eds) Security and Privacy. ISEA-ISAP 2019. Communications in Computer and Information Science, vol 939. Springer, Singapore. https://doi.org/10.1007/978-981-13-7561-3_13
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DOI: https://doi.org/10.1007/978-981-13-7561-3_13
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