@inproceedings{16d3fb05af17438aac6423ecbbc47173,
title = "K-Nearest Neighbours classification based Sybil attack detection in Vehicular networks",
abstract = "In Vehicular networks, privacy, especially the vehicles' location privacy is highly concerned. Several pseudonymous based privacy protection mechanisms have been established and standardized in the past few years by IEEE and ETSI. However, vehicular networks are still vulnerable to Sybil attack. In this paper, a Sybil attack detection method based on k-Nearest Neighbours (kNN) classification algorithm is proposed. In this method, vehicles are classified based on the similarity in their driving patterns. Furthermore, the kNN methods' high runtime complexity issue is also optimized. The simulation results show that our detection method can reach a high detection rate while keeping error rate low.",
keywords = "Intrusion detection, Machine Learning, Sybil Attack, Vehicle Driving Pattern, Vehicular Networking",
author = "Pengwenlong Gu and Rida Khatoun and Youcef Begriche and Ahmed Serhrouchni",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 3rd Conference on Mobile and Secure Services, MOBISECSERV 2017 ; Conference date: 11-02-2017 Through 12-02-2017",
year = "2017",
month = mar,
day = "24",
doi = "10.1109/MOBISECSERV.2017.7886565",
language = "English",
series = "Proceedings of the 2017 3rd Conference on Mobile and Secure Services, MOBISECSERV 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Selwyn Piramuthu and Pascal Urien",
booktitle = "Proceedings of the 2017 3rd Conference on Mobile and Secure Services, MOBISECSERV 2017",
}