A Robust Traffic Information Management System Against Data Poisoning in Vehicular Networks

Abstract

Due to the real-time demand and the amount of processed data, network security attacks are frequent concerns to the systems inspired by vehicular networks. Attacks that act to decrease data exchange reliability, such as Data Poisoning (DaP)attacks, are one of the most damaging. Although existing mechanisms provide data validation and collaborative threats detection, most vehicular network systems do not implement these features. This work presents MOVE, an efficient, secure, and VANET-based traffic management system against DaP attacks. MOVE employs watchdog monitoring along with relational consensus for network attacks detection, aiming for data authenticity and availability. MOVEs performance was evaluated on OMNET++, reaching 90% of detection rate, 4% of false-negative, and 10% of false-positive. Further, MOVE decreases the vehicles travel time by up to 40%, average time lost due to traffic jams by 35%, and MOVE increases the average speed by 12% comparing to ON-DEMAND.