Non-Interactive Detection of Malicious Vehicular Network Data

Vehicular networks might be deployed in the near future, and as a consequence a potentially large number of exciting applications are expected to enhance the human driving experience. Unless the security of such applications is guaranteed, however, such enhancements may be accompanied by similarly powerful and yet undesired consequences in malicious behaviour. While current research in the vehicular networks security area has recognized conventional security and cryptographic threats, detailed modeling and analysis of threats that are specific to vehicle traffic are rarely considered in the literature. In this paper we lay ground for a comprehensive investigation of “traffic-related” threats to vehicular networks. We study the problem of modeling traffic-related attacks in these networks and present automatic and efficient (i.e., no human intervention and no expensive cryptographic protocols) yet general solutions to prevent or tolerate a number of these attacks. Specifically, we propose techniques based on the capability of implementing simple and non-interactive voting algorithms that use the mere participations of vehicles to the network and, while doing that, attempt to maximize use of already exchanged and relevant network data. We validate our techniques by providing analysis results based on both simulated and real-life mobility