Machine Learning-Based Detection of Data Replay and Data Replay Sybil Attacks for Vehicular Communication Networks

Owana Marzia Moushi, Chamath Gunawardena, Feng Ye, Rose Qingyang Hu, Yi Qian

Abstract: A vehicular network is susceptible to various security flaws and attacks. Cryptographic techniques are used in vehicular networks but these alone cannot provide proper security to the network. Identifying various types of attacks is necessary to secure vehicular communication networks. This work is focused on both binary and multi-class attack detection in vehicular networks. A publicly available dataset, VeReMi-Extension is used to detect these attacks. This dataset has been reformulated to generate novel features aimed at detecting attacks in vehicular networks accurately. Machine learning-based methods have been applied to the reformulated dataset for the detection of attacks in vehicular networks. The extensive simulation results show that the proposed scheme can detect more than 99% attacks both for binary and multi-class scenarios which is an impressive performance to enhance the security in vehicular networks.

External IDs:dblp:conf/icc/MoushiGYH024