Detecting and Preventing False Nodes and Messages in Vehicular Ad-Hoc Networking (VANET)

Abstract

Vehicular ad-hoc network (VANET) is an advanced mobile wireless network. The escalation of equipped vehicles on the road grabs the attention of researchers and is constantly striving to take it further. This type of wireless network infrastructure helps to establish communication between vehicles. Roadside units, sensors, and vehicular nodes are the critical components of our work to protect the vehicular network. This research is focused on detecting and preventing fake vehicular nodes and their messages by applying fake node detection and prevention algorithms and counterfeit message detection and prevention algorithms. In our proposed approach, the fake node detection and prevention algorithms check the node profile after establishing the mesh structure. If the profile attribute named "Pen/Rew" satisfies the condition that should be less than or equal to a threshold value (zero), the fake message detection and prevention process starts. The message will be accepted once the situation is satisfied; otherwise, it is declared fake. We utilized ONE (opportunistic network environment) simulator to generate node movement models, route messages between nodes, and visualize mobility and messaging passing in real time. The results indicated that our proposed work perfectly detects and prevents fake nodes and messages.

Privacy-preserving authentication approach for vehicular networks

“…Accident announcement in a VANET scenario A VANET structure is a wireless network in which all vehicles within the proximity of the network can connect as such this raises some security concerns because malicious vehicles can join the network and transmit false messages about traffic conditions and also alter the messages that are being sent in the network which in turn can cause a denial of service attack because the legitimate vehicles can be denied access to a certain route [8]− [10]. The malicious vehicles can also find out the real identities of the vehicles and create an illusion of those identities causing a sybil attack [11], [12]. Malicious vehicles can also trace the location of vehicles as the move around in the network and this poses as a security threat to the legitimate vehicles.…”