Abstract
In vehicular ad hoc networks, inside attackers can launch a false information attack by injecting false emergency messages to report bogus events such as traffic accidents. In this article, a false message detection scheme is proposed and evaluated. First, traffic flow theory is employed to analyze vehicular behavior under a traffic accident scenario. It shows that a “bottleneck” phenomenon is triggered because the road capacity is reduced by blocked lanes at an accident site. The traffic parameters, such as vehicular density, exhibit a distinct statistical property compared to an accident-free scenario. Based on this, a false message detection algorithm is proposed in which the traveling vehicles are exploited as witnesses to collect traffic parameters, and their observation data are used as evidence to feed a traffic flow model. A Bayesian theorem–based method is used to calculate the likelihood for each traffic scenarios, and the actual traffic condition is estimated to determine whether the reported accident has actually occurred. Finally, the performance of the proposed scheme was verified through simulations in a realistic traffic scenario. It was shown that a higher detection accuracy could be obtained compared to previously proposed approach.
Highlights
In recent years, vehicular ad hoc networks (VANETs) have received much attention from academics and industry because a variety of VANETs applications have emerged for road safety, passenger comfort, and traffic efficiency.[1]
In VANETs, vehicles are equipped with wireless access vehicular environment (WAVE) devices, which enable them to communication with each other, and with pre-deployed roadside units (RSUs; vehicle-to-infrastructure, V2I)
Vehicles are exploited as a ‘‘moving sensor’’ for collecting traffic information, and they are allowed to broadcast two types of messages: (1) periodic beacon messages, which are used to show the present of a vehicle in the network, and (2) emergency messages, which are used to report the occurrence of damaging events
Summary
Vehicular ad hoc networks (VANETs) have received much attention from academics and industry because a variety of VANETs applications have emerged for road safety, passenger comfort, and traffic efficiency.[1]. Applying a centralized reputation/trust mechanism in VANETs has long been debatable To solve this problem, data-centric detection schemes have been proposed to reduce the network overhead and improve efficiency using fully distributed and localized detection algorithms,[8,9,10] in which the vehicles located at the scene are exploited as ‘‘witness,’’ and they perform cooperatively and locally the detection algorithm to collect possible evidence for verifying the correctness of safety messages. A false message detection algorithm is proposed, in which the vehicles located at the scene are exploited as witnesses to infer the actual traffic condition Based on their observational data, a Bayesian approach is used to calculate the likelihood of each traffic pattern and to determine whether the accident reported by the emergency message has happened. The results of performance evaluation are presented in section ‘‘Simulation.’’ we draw our conclusions in section ‘‘Conclusion.’’
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