Trustworthy communication in vehicular ad-hoc networks is essential to provide functional and reliable traffic safety and efficiency applications. A Sybil attacker that is simulating "ghost vehicles" on the road, by sending messages with faked position statements, must be detected and excluded permanently from the network. Based on misbehavior detection systems, running on vehicles and roadside units, a central evaluation scheme is proposed that aims to identify and exclude attackers from the network. The proposed algorithms of the central scheme are using trust and reputation information provided in misbehavior reports in order to guarantee long-term functionality of the network. A main aspect, the scalability, is given as misbehavior reports are created only if an incident is detected in the VANET. Therefore, the load of the proposed central system is not related to the total number of network nodes. A simulation study is conducted to show the effective and reliable detection of attacker nodes, assuming a majority of benign misbehavior reporters. Extensive simulations show that a few benign nodes (at least three witnesses) are enough to significantly decrease the fake node reputation and thus identify the cause of misbehavior. In case of colluding attackers, simulations show that if 37% of neighbor nodes cooperate, then an attack could be obfuscated
Most applications considered in Vehicular Adhoc Networks (VANETs) base their calculations on the location of vehicle and roadside units. Therefore, the trustworthiness of this data is essential in Intelligent Transport System (ITS) and can be addressed by digitally signing sent location information. However, we have to assume that an attacker is able to get valid secret keys and she or he thus may send authenticated messages with faked mobility information. In this work we therefore do not rely on encryption techniques only. Instead, we propose a novel framework for verifying mobility data, which aims at detecting messages representing non-plausible movement behaviour. A Kalman filter is exploited to detect malicious behaviour based on past vehicle movements. Regular changes of vehicle identifiers in the communication range due to privacy protection are made transparent in the mobility data verification framework. In order to enhance the framework, additional information from environmental sensors is integrated. To prove accuracy of our model, replaying of recorded traces and test drives were carried out.
Vehicular Ad-hoc Networks (VANETs) aim to increase, among others, traffic safety and efficiency by warning and informing the driver about road events and hazards. Due to their direct impact on drivers' safety, external and internal attacks have to be prevented. While authentication prevents most of the external attacks, internal attackers are still able to misuse the system and inject fake - but authenticated - messages. Therefore, misbehavior detection and prevention mechanisms are required to mitigate such attacks. In this paper we provide a categorization of internal attackers to identify most relevant attack variants. Instead of using simulations, as done by most related works, we use an implementation on real vehicles to demonstrate the feasibility of location-based attacks. Especially, we demonstrate that a malware application installed on a vehicle can provoke false warnings on benign vehicles that are within the attacker's communication range. This exemplary att ack is possible due to insufficiently specified VANET security standards. By using our proposed countermeasures, we show that this internal attack is detected and blocked, preventing false driver warnings
A malicious attack in which bogus information is distributed in a vehicular ad hoc network may have notably effect on the traffic efficiency. The reliability and trustworthiness of a VANET is very important, especially in the deployment phase. As long as the density of vehicles on the road, equipped with a VANET communication system, is relatively low, the introduction of bogus traffic information by attackers may have substantial impact due to the lack of vehicles that are able to disprove such faked information. As result, vehicle drivers may react corresponding to a displayed danger warning and therewith thwart the road traffic unnecessarily. We present possible ways to simulate stationary and moving attackers in order to show their effect on the traffic efficiency by considering appropriate driver behavior. In order to have realistic traffic and communication behavior, the simulation runtime infrastructure VSimRTI is used.
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