Attack Classification and Detection for Misbehaving Vehicles using ML/DL
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Abstract
Vehicle ad hoc networks are a crucial component of the next Intelligent Transportation System created to build a reliable and secure connection between various network components to establish a safe and effective transportation network. Because of open nature of VANETs become vulnerable to numerous assaults such forgery, Denial-of-Service (DoS), and false reports, which can ultimately cause traffic jams or accidents The earlier study concentrated on misbehaving vehicles rather than RSUs. Proposed method integrates data from two subsequent BSMs for testing and training by employing machine learning (ML) methods. The framework merges the data from two BSMs in the right manner and utilizes machine learning/Deep learning methodology which identify the running vehicle as a legal or hostile one.
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“Global status report on road safety 2018,” Accessed: Jan. 5, 2021.[Online].Available:http://apps.who.int/iris/bitstream/handle/10665/277370/WHO-NMH-NVI-18.20-eng.pdf?ua=1Re
S. Al-Sultan, M. M. Al-Doori, A. H. Al-Bayatti, and H. Zedan, “A comprehensive survey on vehicular Ad Hoc network,” J. Netw. Comput.Appl., vol. 37, pp. 380–392, 2014.
S.-h. An, B.-H. Lee, and D.-R. Shin, “A survey of intelligent transportation systems,” in Proc. 3rd Int. Conf. Comput. Intell., Commun. Syst.Netw., 2011, pp. 332–337.
M. N. Mejri, J. Ben-Othman, and M. Hamdi, “Survey on VANET security challenges and possible cryptographic solutions,” Vehicular Communications, vol. 1, no. 2, pp. 53–66, 2014.
Yasam, S. ., H. Nair, S. A. ., & Kumar, K. S. . (2023). Machine Learning based Robust Model for Seed Germination Detection and Classification . International Journal of Intelligent Systems and Applications in Engineering, 11(2s), 116–124. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2515
S. Gyawali, S. Xu, Y. Qian, and R. Q. Hu, “Challenges and Solutions for Cellular Based V2X Communications,” IEEE Communications Surveys & Tutorials, vol. 23, 2020.
Mitchell, Robert, and Ing-Ray Chen. "A survey of intrusion detection techniques for cyber-physical systems." ACM Computing Surveys (CSUR) 46, no. 4 (2014): 1-29.
S. S. Tangade and S. S. Manvi, “A survey on attacks, security and trust management solutions in VANETs,” in Proceedings of the 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), pp. 1–6, Tiruchengode, India, July 2013.
M. S. Sheikh, J. Liang, and W. Wang, “Security and privacy in vehicular ad hoc network and vehicle cloud computing: a survey,” Wireless Communications and Mobile Computing, vol. 2020, Article ID 5129620, 2020.
S. Gyawali and Y. Qian, “Misbehavior detection using machine learning in vehicular communication networks,” in Proceedings of the ICC 2019-2019 IEEE International Conference on Communications (ICC), pp. 1–6, Shanghai, China, May 2019.
Z. A. Biron, S. Dey, and P. Pisu, “Real-time detection and estimation of denial of service attack in connected vehicle systems,” IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 12, pp. 3893–3902, 2018.
Z. Yang, K. Zhang, L. Lei, and K. Zheng, “A novel classifier exploiting mobility behaviors for Sybil detection in connected vehicle systems,” IEEE Internet of Things Journal, vol. 6, no. 2, pp. 2626–2636, 2018.
D. He, S. Zeadally, B. Xu, and X. Huang, “An efficient identity-based conditional privacy-preserving authentication scheme for vehicular ad hoc networks,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 12, pp. 2681–2691, 2015.
F. Qu, Z. Wu, F.-Y. Wang, and W. Cho, “A security and privacy review of VANETs,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 6, pp. 2985–2996, 2015.
A. K. Malhi, S. Batra, and H. S. Pannu, “Security of vehicular ad-hoc networks: a comprehensive survey,” Computers & Security, vol. 89, Article ID 101664, 2020.
Khatri, Sahil; Vachhani, Hrishikesh; Shah, Shalin; Bhatia, Jitendra; Chaturvedi, Manish; Tanwar, Sudeep; Kumar, Neeraj (2020). Machine learning models and techniques for VANET based traffic management: Implementation issues and challenges. Peer-to-Peer Networking and Applications, (), –. doi:10.1007/s12083-020-00993-4.
Ms.Nivedita Kadam and Dr. Krovi Raja Sekhar. Machine Learning Approach of Hybrid KSVN Algorithm to Detect DDoS Attack in VANET. (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 12, No. 7, 2021.
Ammar Haydari and Yasin Yilmaz. Real-Time Detection and Mitigation of DDoS Attacks in Intelligent Transportation Systems. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) Maui, Hawaii, USA, November 4-7, 2018.
A. Sharma and A. Jaekel, "Machine Learning Based Misbehaviour Detection in VANET Using Consecutive BSM Approach," in IEEE Open Journal of Vehicular Technology, vol. 3, pp. 1-14, 2022, doi: 10.1109/OJVT.2021.3138354.
Agria Rhamdhan , Fadhil Hidayat. Hybrid Trust-based Defense Mechanisms Against Sybil Attack in Vehicular Ad-hoc Networks. MSCEIS 2019, October 12, Bandung, Indonesia Copyright © 2020 EAI DOI 10.4108/eai.12-10-2019.2296524.
Sharshembiev, K., Yoo, S.-M., & Elmahdi, E. (2021). Protocol misbehavior detection framework using machine learning classification in vehicular Ad Hoc networks. Wireless Networks, 27(3), 2103–2118. doi:10.1007/s11276-021-02565-7.
Singh, P. K., Gupta, S., Vashistha, R., Nandi, S. K., & Nandi, S. (2019). Machine Learning Based Approach to Detect Position Falsification Attack in VANETs. Security and Privacy, 166–178. doi:10.1007/978-981-13-7561-3_13.
Alsarhan, Ayoub; Al-Ghuwairi, Abdel-Rahman; Almalkawi, Islam T.; Alauthman, Mohammad; Al-Dubai, Ahmed (2020). Machine Learning-Driven Optimization for Intrusion Detection in Smart Vehicular Networks. Wireless Personal Communications, (), –. doi:10.1007/s11277-020-07797-y.
Abhilash Sonker, R. K. Gupta. A new procedure for misbehavior detection in vehicular ad-hoc networks using machine learning. International Journal of Electrical and Computer Engineering (IJECE) Vol. 11, No. 3, June2021, pp. 2535~2547 ISSN: 2088-8708, DOI: 10.11591/ijece.v11i3.pp2535-2547.
Heena Khanna and Manmohan Sharma. An Improved Security Algorithm for VANET using Machine Learning. Journal of Positive School Psychology 2022, Vol.6, No.3, 7743 – 7756.
Bangui, H., Ge, M., & Buhnova, B. (2022). A hybrid machine learning model for intrusion detection in VANET. Computing, 104(3), 503-531.
Bangui, H., Ge, M., & Buhnova, B. (2021). A hybrid data-driven model for intrusion detection in VANET. Procedia Computer Science, 184, 516-523.
Shams, E. A., Rizaner, A., & Ulusoy, A. H. (2018). Trust aware support vector machine intrusion detection and prevention system in vehicular ad hoc networks. Computers & Security, 78, 245-254.
Schmidt, D. A., Khan, M. S., & Bennett, B. T. (2020). Spline?based intrusion detection for VANET utilizing knot flow classification. Internet Technology Letters, 3(3), e155.
Alshammari, A., Zohdy, M. A., Debnath, D., & Corser, G. (2018). Classification approach for intrusion detection in vehicle systems. Wireless Engineering and Technology, 9(4), 79-94.
Saleha Saudagar, Radhika P Fuke, Sonika A Chorey, Gayatri Jagnade, “Smart parking stratagem based on IOT,” 2019 Cikitusi Journal For Multidisciplinary Research, Volume 6, Issue 4, April 2019, ISSN NO: 0975-6876.
Saudagar, S.I., Chorey, S.A., Jagnade, G.A. (2019). Review on Intrigue Used for Caching of Information in View of Information Density in Wireless Ad Hoc Network. In: Springer, Singapore. https://doi.org/10.1007/978-981-13-1501-5_59
Tarwani, Kanchan M., Saleha S. Saudagar, and Harshal D. Misalkar. "Machine learning in big data analytics: an overview." International Journal of Advanced Research in Computer Science and Software Engineering 5.4 (2015): 270-274.
Aboelfottoh, A. A., & Azer, M. A. (2022, May). Intrusion Detection in VANETs and ACVs using Deep Learning. In 2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) (pp. 241-245).IEEE