The integrity of messages in vehicular ad-hoc networks has been extensively studied by the research community, resulting in the IEEE&#126;1609.2 standard, which provides typical integrity guarantees. However, the correctness of message contents is still one of the main challenges of applying dependable and secure vehicular ad-hoc networks. One important use case is the validity of position information contained in messages: position verification mechanisms have been proposed in the literature to provide this functionality. A more general approach to validate such information is by applying misbehavior detection mechanisms. In this paper, we consider misbehavior detection by enhancing two position verification mechanisms and fusing their results in a generalized framework using subjective logic. We conduct extensive simulations using VEINS to study the impact of traffic density, as well as several types of attackers and fractions of attackers on our mechanisms. The obtained results show the proposed framework can validate position information as effectively as existing approaches in the literature, without tailoring the framework specifically for this use case.
Stübing et al. @cite_11 have proposed a different approach; rather than developing a generic framework for misbehavior detection, they have developed a framework to combine several information sources that are all concerned with correctness of position and movement information. Specifically, their approach combines several autonomous data-centric mechanisms (path prediction and maneuver recognition), which allow them to accurately predict the movement of neighboring vehicles. However, their work has two main disadvantages: it cannot detect certain types of attacks (e.g., when the attacker consistently falsifies her position by a fixed vector), and it cannot be extended or combined to work with other detectors without further work. In our framework, it is possible to integrate their detection results, and the concrete detectors we improve in this paper can detect exactly the attack that the framework by Stübing et al. @cite_11 cannot.
@cite_11
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Security is a prerequisite for successfully establishing Car-to-X technology on the market. Sender authentication, message integrity as well as plausibility of the message content represent one of the key aspects in this domain. While the first two requirements are covered by means of cryptography, the latter is still subject to current research. Recent works in the area of vehicle behavior analysis include promising approaches, but are still prone to errors in case of highly dynamic driving maneuvers. In this work we present a novel two-stage verification process for reliable Car-to-X mobility data verification. The first stage consists of a mobility estimator realized by a Kalman filter. The Kalman filter is thereby used to evaluate received mobility data with respect to the path history of vehicles. In the second stage a plausibility check for highly dynamic traffic situations is applied using probabilistic traffic maneuver recognition based on Hidden Markov Models. The overall framework is implemented and its effectiveness is evaluated by means of real world experiments.