A vehicular ad hoc networks intrusion detection system based on BUSNet

The open medium, dynamic topology, and multi-hop cooperative routing of vehicular ad hoc networks (VANETs) make it facing more security challenge than wired networks. In this paper, a hierarchical VANETs intrusion detection system based on BUSNet is present. BUSNet is basically a virtual mobile backbone infrastructure that is constructed using public buses. We use the bus nodes as the cluster-heads to gather the routing control messages and data packets transmitted among the vehicles. The bus nodes first transmit the original network behavior information to the access points deployed along the road sides. Then the access points can get a global view of the VANETs, and we can detect anomaly behaviors through analyzing the data. The anomaly detection method is based on the neural network which can build the normal network behavior model through learning process. After the trained neural network is stable, it can monitor the VANETs security by detecting the network control message and data packet in real time and alarm immediately if there is anomaly behavior. The experiments in NS2 show that the detection method can detect anomaly behavior with low false alarm rate.