Detecting Bogus Information Attack in Vehicular Ad Hoc Network: A Context-Aware Approach

Abstract

Most of Vehicular Ad Hoc Network’s (VANET) and Fly Ad Hoc Network (FANET) applications, protocols, and service rely on cooperativeness among network participants. Vehicles, could be unmanned aerial vehicles (e.g. drones), share their sensors’ information to improve network performance and provide safety, traffic efficiency, and entertainments. The accuracy and reliability of this information are vital for VANET performance. However, misbehaving (or faulty) vehicles, which exploit the cooperativeness feature by sharing bogus information, can disrupt the fundamental operations of any potential application and cause loss of people lives and properties. Unfortunately, existing detection approaches cannot effectively thwart such attacks. The highly dynamic vehicular context has been vastly neglected as well as they rely on predefined and static security perimeters to differentiate between false and correct information. In this paper, a context-aware detection approach is proposed to improve the detection accuracy and reduce the false alarm rate. Firstly, the features that well represent the vehicular context has been extracted. Then, an online unsupervised learning method namely the hierarchical clustering algorithm is used to classify the received messages into either genuine or spurious. Finally, Bayesian-based hypothesis testing has been used to confirm the validity of the classification. Results show that the proposed detection approach is promising in effectively detect bogus information attack and improve applications performance.