Non-interactive malicious behavior detection in vehicular networks

Abstract

We lay ground for a comprehensive investigation of "traffic-related" threats to vehicular networks. While current research in the vehicular networks security area has done a good job in recognizing standard 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 study the problem of modeling traffic-related attacks in vehicular networks and presenting automatic and efficient (i.e., no human intervention and no expensive cryptographic protocols) solutions to prevent or tolerate a number of these attacks. To prevent these attacks, we propose techniques based on the capability of implementing simple and non-interactive voting algorithms using the mere participations of vehicles to the vehicular network. We provide analysis and simulation results in typical urban environments validating our techniques. Previous work required interactive protocols to implement voting or consensus techniques and implicitly left open the question we solve in this paper.A 2010 IEEE Vehicular Networking Conference 978-1-4244-9525-2/10/$26.00 ©2010 IEEE 278 assigned geographic area, using wireless communication. RSEs may, or, most likely, may not cover the entire geographic map. Naturally, we also consider a backbone network that communicates with RSEs and, in special situations with OBEs. We assume that a public-key infrastructure is in place, and that the vehicle-to-vehicle communication is authenticated using digital signatures and certificates managed by a back-end certificate authority (CA), possibly using RSE as front-end CA servers (see, e.g., [5,6] for more details on how such an infrastructure could be deployed). B. Traffic-Related Malicious BehaviorMalicious OBE behaviors related to traffic activity include attempting to use the compromised vehicle clients to send malicious messages to other vehicles or RSEs; incapacitate crucial vehicular network functions (e.g., its key and certificate management system and hence its privacy preserving functions); harm, disrupt, or destroy portions of the vehicular network, and the public images of the network providers and the vehicle manufacturers.

A New Detection Jamming Attack Model on VANET Ad hoc Networks

“…The paper didn't include detection; therefore, it's assumed it's beyond the scope of the study. [13] G. Di Crescenzo et al, creates a voting method that uses vehicle involvement to prevent malicious data manipulation, fabrication, or modification of the operation of a vehicle's on-board equipment in order to carry out attacks [14] On 802.11 networks, Sampath and colleagues investigated the impact and feasibility of cognitive radio-based jamming attacks. They demonstrated how the attacker can utilize the quick channel switching capability of cognitive radios to increase the jamming effect by utilizing a single radio to jam many channels.…”

Increasing Survivability of DSRC Safety Applications through Dissimilarity and Redundancy without Altering Existing Standards

“…As an example of the first kind, redundancy was utilized in [8], where a non-interactive voting algorithm performed by the vehicle was introduced to detect malicious behavior. The algorithm depends on BSM messages broadcasts from other vehicles' reaction to an event to infer on the truth in that event.…”

The Impact of Dissimilarity and Redundancy on the Reliability of DSRC Safety Applications

“…As an example of the first kind, redundancy was utilized in [9], where a non-interactive voting algorithm performed by the vehicle was introduced to detect malicious behavior. The algorithm depends on BSM message broadcasts from other vehicles' reaction to an event to infer on the truth in that event.…”