Position Falsification Detection Approach Using Travel Distance-Based Feature

1 Kim, J.-W., Kim, J.-W. and Jeon, D.-K. (2018) A cooperative communication protocol for QoS provisioning in ieee 802.11 p/wave vehicular networks. Italian National Conference on Sensors. DOI:10.3390/s18113622. Search in Google Scholar

2 Rajkumar, M.N., Nithya, M. and HemaLatha, P. (2016) Overview of Vanet with its Features and Security Attacks. International Research Journal of Engineering and Technology (IRJET), 3(1), 137–142. Search in Google Scholar

3 Bassiony, I.S. and Salama, G. (2022) Detection approaches for position falsification attack in VANET. In: 13th International Conference on Electrical Engineering (ICEENG). DOI:10.1109/ICEENG49683.2022.9781915. Search in Google Scholar

4 Govindan, H., Jacob, L., Babu, A.V. (2011) Bit-based fairness in ieee802. 11p mac for vehicle-to-infrastructure networks. In: Proceedings of the 2011 international conference on Advanced Computing, Networking and Security. DOI:10.1007/978-3-642-29280-4_39. Search in Google Scholar

5 Azees, M., Vijayakumar, P. and Jegatha Deborah, L. (2016) Comprehensive survey on security services in vehicular ad-hoc networks. IET Intelligent Transport Systems Journal, 10(6), 379–388. DOI:10.1049/iet-its.2015.0072. Search in Google Scholar

6 Sumra, I.A., Ahmad, I., ab Manan, J.-L., Hasbullah, H. (2011) Behavior of attacker and some new possible attacks in vehicular ad hoc network (vanet). In: The 3rd International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2011, IEEE, 1–8. Search in Google Scholar

7 Bißmeyer, N., Njeukam, J., Petit, J., & Bayarou, K. M. (2012) Central misbehavior evaluation for VANETs based on mobility data plausibility. In: VANET '12: Proceedings of the ninth ACM international workshop on Vehicular inter-networking, systems, and applications, 73–82. https://doi.org/10.1145/2307888.2307902. Search in Google Scholar

8 Grover, J., Prajapati, N. K., Laxmi, V., & Gaur, M. S. (2011) Machine learning approach for multiple misbehavior detection in VANET. In: Abraham, A., Mauri, J.L., Buford, J.F., Suzuki, J., Thampi, S.M. (eds) Advances in Computing and Communications. ACC 2011. Communications in Computer and Information Science, 192. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22720-2_68. Search in Google Scholar

9 So, S., Sharma, P., & Petit, J. (2018) Integrating Plausibility Checks and Machine Learning for Misbehavior Detection in VANET. In: The 17th IEEE International Conference on Machine Learning and Applications (ICMLA). DOI: 10.1109/ICMLA.2018.00091. Search in Google Scholar

10 Sharma, P., Austin, D., & Liu, H. (2019) Attacks on machine learning: Adversarial examples in connected and autonomous vehicles. In: 2019 IEEE International Symposium on Technologies for Homeland Security (HST). DOI: 10.1109/HST47167.2019.9032989. Search in Google Scholar

11 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, 939. Springer, Singapore. https://doi.org/10.1007/978-981-13-7561-3_13. Search in Google Scholar

12 So, S., Petit, J., & Starobinski, D. (2019) Physical layer plausibility checks for misbehavior detection in V2X networks. In: WiSec '19: Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks, 84–93. https://doi.org/10.1145/3317549.3323406. Search in Google Scholar

13 Kamel, J., Jemaa, I. B., Kaiser, A., Cantat, L., & Urien, P. (2019) Misbehavior detection in C-ITS: A comparative approach of local detection mechanism. In: 2019 IEEE Vehicular Networking Conference (VNC). DOI: 10.1109/VNC48660.2019.9062831. Search in Google Scholar

14 Gyawali, S., & Qian, Y. (2019) Misbehavior Detection using Machine Learning in Vehicular Communication Networks. In: ICC 2019 - 2019 IEEE International Conference on Communications (ICC). doi: 10.1109/ICC.2019.8761300. Search in Google Scholar

15 Hawlader, F., Boualouache, A., Faye, S., & Engel, T. (2021) Intelligent misbehavior detection system for detecting false position attacks in vehicular networks. In: 2021 IEEE International Conference on Communications Workshops (ICC Workshops). DOI: 10.1109/ICCWorkshops50388.2021.9473606. Search in Google Scholar

16 Gonçalves, F., Macedo, J., & Santos, A. (2021) An intelligent hierarchical security framework for VANETs. Information 12(11), 455. DOI:10.3390/info12110455. Search in Google Scholar

17 Sonker, A., & Gupta, R. (2021) A new procedure for misbehavior detection in vehicular Ad-Hoc networks using machine learning. In: International Journal of Electrical and Computer Engineering (IJECE), 11(3), 2535–2547. DOI:10.11591/ijece.v11i3. Search in Google Scholar

18 Amanullah, M. A., Chhetri, M. B., Loke, S. W., Doss, R., & BurSTADMA. (2022) Towards an Australian dataset for misbehaviour detection in the internet of vehicles. In: 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). DOI: 10.1109/PerComWorkshops53856.2022.9767505. Search in Google Scholar

19 Sharma, A., & Jaekel, A. (2022) Machine learning based misbehaviour detection in VANET using consecutive BSM approach. IEEE Open Journal of Vehicular Technology, (99), 1-1. DOI:10.1109/OJVT.2021.3138354. Search in Google Scholar

20 Ercan, S., Ayaida, M., & Messai, N. (2022) Misbehavior detection for position falsification attacks in VANETs using machine learning. In: The 2nd International Conference on Electronics and Communication Systems (ICECS), EEE Access Journal, (99), 1-1. http://dx.doi.org/10.1109/ACCESS.2021.3136706. Search in Google Scholar

21 Haydari, A., & Yilmaz, Y. (2018) Real-time detection and mitigation of DDoS attacks in intelligent transportation systems. In: 21st International Conference on Intelligent Transportation Systems (ITSC), 1–20. DOI: 10.1109/ITSC42983.2018. Search in Google Scholar

22 Raghuwanshi, V., & Jain, S. (2015) Denial of service attack in VANET: A survey. International Journal of Engineering Trends and Technology (IJETT), 28(1). DOI: 10.14445/22315381/IJETT-V28P204. Search in Google Scholar

23 Heijden, R. W., Lukaseder, T., & Kargl, F. (2018) VeReMi: A dataset for comparable evaluation of misbehavior detection in VANETs. In: ICC 2020-2020 IEEE International Conference on Communications (ICC). DOI:10.1109/ICC40277.2020.9149132. Search in Google Scholar

24 Codeca, L., Frank, R., Faye, S., & Engel, Th. (2017) Luxembourg SUMO traffic (LuST) scenario: traffic demand evaluation. IEEE Intelligent Transportation Systems Magazine, 9(2). DOI:10.1109/MITS.2017.2666585. Search in Google Scholar