Security Measures with Enhanced Behavior Processing and Footprint Algorithm against Sybil and Bogus Attacks in Vehicular Ad Hoc Network

Abstract

Vehicular ad hoc networks (VANETs) are created according to the principles of ad hoc mobile networks (MANETs), i.e., spontaneous creation of a wireless network for vehicle-to-vehicle (V2V) communication. Each vehicle in this network is treated as a node that is part of the mobile network. VANET turns all cooperating vehicles into a wireless router or node. This makes it possible to connect all cars within range to a stationary unit and create a wide network with a huge range. VANET is widely used for better traffic management, vehicle-to-vehicle communication, and road information provision. The VANET network is exposed to identity and information attacks, concealing or delaying data transmission, or information theft. Therefore, there are multiple types of attack, such as Sybil or bogus, that might harm the whole network infrastructure. The consequences of the mentioned two attacks could lead not only to the given infrastructure but could cause hammering people’s lives. In this paper, we analyze the ongoing methods for preserving Sybil and bogus attacks in a VANET network together with the authors’ methods: the Bogus & Sybil Trust Level & Timestamp (B&STL&T) algorithm and the Bogus & Sybil Enhanced Behavior Processing & Footprint (B&SEBP&F) algorithm. The first algorithm, the Bogus & Sybil Trust Level & Timestamp (B&STL&T) algorithm was improved into the Bogus & Sybil Enhanced Behavior Processing & Footprint (B&SEBP&F), presented in the paper. The proposed methods were tested with multiple scenarios using different variations of bogus and Sybil attack and various attacker–victim node number ratios. During analysis, it was observed that detection of all attackers in the network was reduced by approximately 30% in comparison to previous work and that of other cited authors.