Dealing with Smart GPS Spoofing Attacks in VANETs: 3BSM Approach

Zahin Wahab, Bishal Basak Papan, Md. Shohrab Hossain, Mohammed Atiquzzaman

Abstract: Nowadays VANETs are being used to ensure road safety and reduce traffic congestion. Vehicles exchange real time position with one another through basic safety messages and these periodic updates must be authenticated for safety purpose. Malicious vehicles can broadcast spoofed position and such an insider attack cannot be dealt with cryptography and digital signature. However, previous machine learning-based approaches that used distributed data-centric misbehaviour detection schemes could detect such attacks in most of the scenarios. However, in case of smarter attacks in a sparse network (where the attacker vehicle pretend to be stand still even though it is on the move), previous solutions could not always detect such attacks. We propose a novel detection scheme which outperforms previously published methods in this specific scenario. For evaluating the performance of this approach, we have used VeReMi dataset, a public repository for the malicious node detection in VANETs. We combine three consecutive basic safety messages and surpass currently existing methods in detecting eventual stop attacks, especially in sparse networks. Our proposed method can help in securing VANET, thereby preventing fatal accidents.

External IDs:dblp:conf/icc/WahabPHA24