Identity Spoofing Sybil Attack Protective Measures using Physical & Logical Address Mapping for the VANET (ISPLM)

Authors

  • Shubhra Mukherjee Mathur Department of Computer Science and Engineering, Sarvepalli Radakrishnan University ,Bhopal MIT-Art, Design and Technology University,Pune
  • Ravindra Gupta Department of Computer Engineering Sarvepalli Radakrishnan University ,Bhopal

Keywords:

VANET, Sybil, Routing, Security, AODV

Abstract

Vehicular Ad Hoc Networks (VANETs) is a kind of ad hoc network that enables communication among vehicle to vehicle (V2V) and between vehicles and roadside infrastructure (V2I).  VANETs are a sub class of mobile ad hoc network (MANET) and have unique characteristics and challenges due to the dynamic nature of vehicular environment. The main challenges such as topology unsustainable and security, due to movement of vehicle is dynamic which directly or indirectly monitored or guided by road side units (RSUs) somehow resolve the problem of topology. Other side vehicular ad hoc network is unsecure due to various attack spread into network such are traffic jamming, denial of service, routing misbehavior, identity spoofing etc. In the previous research, the a few of security issues are resolve through the hardware or software based approach, out of above insecurity identity spoofing is one of the major challenging attack because its generate false identity to capture the data packet as a legitimate user. In this article resolve the issue of identity spoofing attack using physical & logical address mapping (ISPLM), the identity spoofing is a kind of sybil attack where vehicle capture the identity of other vehicle and represent as a genuine receiver in the network. Sybil attacks are two types which are same time multiple identities and different time generates multiple identities to gain the network data and spread insecurity in VANET. The proposed ISPLM security technique is inbuilt in the RSU’s which keep records of every vehicle related to logical into physical address mapping and if they found multiple logical address mapped with one physical address than detected identity treated as attacker vehicle and block from the communication.  The overall security system produce secure communication which finally compare with existing AODV-WA and AODV-Sybil and get analysis in terms of throughput, packet delivery ratio, routing overhead, true false positive ratio and infection percentage. 

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References

Bo Yu, Cheng-Zhong Xu, Bin Xiao “Detecting Sybil attacks in VANETs”, Elsevier Inc, J. Parallel Distrib. Comput. 73 (2013) 746–756.

Muhammad Iqbal Younis, Rana M. Amir Latif, Izharul Haq, NZ Jhanjhi, Abdul Karim, “An Evaluation of Sybil Attack’s Detection Approaches in Vehicular Ad-Hoc Networks (VANETs)”, International Journal of Intelligent Systems and Applications in Engineering IJISAE, 2022, 10(2s), 124–133

Park, S., et al., Defense against Sybil attack in the initial deployment stage of vehicular ad hoc network based on roadside unit support. Security and Communication Networks, 2013. 6(4): p. 523- 538.

Salam Hamdan, Amjad Hudaib, Arafat Awajan [4] Detecting Sybil attacks in vehicular ad hoc networks”, Networking and Internet Architecture, arXiv:1905.03507, Year-2019.

Pattanayak, Binod & Pattnaik, Omkar & Pani, Sasmita. (2021). Dealing with Sybil Attack in VANET. 10.1007/978-981-15-5971-6_51, In book: Intelligent and Cloud Computing (pp.471-480).

Sefati, Seyed Salar & Ghiasi, Sara, “Detecting Sybil Attack in Vehicular Ad-hoc Networks (Vanets) by Using Fitness Function, Signal Strength Index and Throughput”. Wireless Personal Communications. 123. 10.1007/s11277-021-09261-x, Year-2022.

Quevedo, Carlos H. O. O. et al. “An Intelligent Mechanism for Sybil Attacks Detection in VANETs.” ICC 2020 - 2020 IEEE International Conference on Communications (ICC) (2020): 1-6.

Azam, S.; Bibi, M.; Riaz, R.; Rizvi, S.S.; Kwon, S.J. Collaborative Learning Based Sybil Attack Detection in Vehicular AD-HOC Networks (VANETS). Sensors 2022, 22, 6934. https://doi.org/10.3390/s22186934

Zhou, T.; Choudhury, R.R.; Ning, P.; Chakrabarty, K. P2DAP—Sybil Attacks Detection in Vehicular Ad Hoc Networks. IEEE J. Sel. Areas Commun. 2011, 29, 582–594.

Reddy, D.S.; Bapuji, V.; Govardhan, A.; Sarma, S.S.V.N. Sybil attack detection technique using session key certificate in vehicular ad hoc networks. In Proceedings of the 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET), Chennai, India, 16–18 February 2017; pp. 1–5.

Gu, P.; Khatoun, R.; Begriche, Y.; Serhrouchni, A. Support Vector Machine (SVM) Based Sybil Attack Detection in Vehicular Networks. In Proceedings of the 2017 IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, CA, USA, 19–22 March 2017; pp. 1–6.

Gu, P.; Khatoun, R.; Begriche, Y.; Serhrouchni, A. k-Nearest Neighbours classification based Sybil attack detection in Vehicular networks. In Proceedings of the 2017 Third International Conference on Mobile and Secure Services (MobiSecServ), Miami Beach, FL, USA, 11–12 February 2017.

Helmi, Z.; Adriman, R.; TYArif Walidany, H.; Fatria, M. Sybil Attack Prediction on Vehicle Network Using Deep Learning|Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi). August 202).

Laouiti, D.E.; Ayaida, M.; Messai, N.; Najeh, S.; Najjar, L.; Chaabane, F. Sybil Attack Detection in VANETs using an AdaBoost Classifier. In Proceedings of the 2022 International Wireless Communications and Mobile Computing (IWCMC), Dubrovnik, Croatia, 30 May–3 June 2022; pp. 217–222.

Akshaya, K.; Sarath, T.V. Detecting Sybil Node in Intelligent Transport System. In Innovative Data Communication Technologies and Application; Springer: Singapore, 2022; pp. 595–607.

Velayudhan, N.C.; Anitha, A.; Madanan, M. Sybil attack detection and secure data transmission in VANET using CMEHA-DNN and MD5-ECC. J. Ambient Intell. Humaniz. Comput. 2021.

Nirbhay Kumar Chaubey & Dhananjay Yadav, “Detection of Sybil attack in vehicular ad hoc networks by analyzing network performance”, International Journal of Electrical and Computer Engineering (IJECE)Vol. 12, No. 2, April2022, pp. 1703~1710[1] Bo Yu, Cheng-Zhong Xu, Bin Xiao “Detecting Sybil attacks in VANETs”, Elsevier Inc, J. Parallel Distrib. Comput. 73 (2013) 746–756.

Soni, G., Chandravanshi, K., Jhariya, M.K., Rajput, A. (2022). An IPS Approach to Secure V-RSU Communication from Blackhole and Wormhole Attacks in VANET. In: Sarma, H.K.D., Balas, V.E., Bhuyan, B., Dutta, N. (eds) Contemporary Issues in Communication, Cloud and Big Data Analytics. Lecture Notes in Networks and Systems, vol 281. Springer, Singapore. https://doi.org/10.1007/978-981-16-4244-9_5

Soni, G., Chandravanshi, K. (2022). A Novel Privacy-Preserving and Denser Traffic Management System in 6G-VANET Routing Against Black Hole Attack. In: Karrupusamy, P., Balas, V.E., Shi, Y. (eds) Sustainable Communication Networks and Application. Lecture Notes on Data Engineering and Communications Technologies, vol 93. Springer, Singapore. https://doi.org/10.1007/978-981-16-6605-6_49

Soni, G., Chandravanshi, K., Kaurav, A.S., Dutta, S.R. (2022). A Bandwidth-Efficient and Quick Response Traffic Congestion Control QoS Approach for VANET in 6G. In: Saini, H.S., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 385. Springer, Singapore. https://doi.org/10.1007/978-981-16-8987-1_1

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Published

24.03.2024

How to Cite

Mathur, S. M. ., & Gupta, R. . (2024). Identity Spoofing Sybil Attack Protective Measures using Physical & Logical Address Mapping for the VANET (ISPLM) . International Journal of Intelligent Systems and Applications in Engineering, 12(19s), 638–646. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5108

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Research Article