Real-Time Detection and Mitigation of DDoS Attacks in Intelligent Transportation Systems

Vehicular network (VANET), a special type of ad-hoc network, provides communication infrastructure for vehicles and related parties, such as road side units (RSU). Secure communication concerns are becoming more prevalent with the increasing technology usage in transportation systems. One of the major objectives in VANET is maintaining the availability of the system. Distributed Denial of Service (DDoS) attack is one of the most popular attack types aiming at the availability of system. We consider the timely detection and mitigation of DDoS attacks to RSU in Intelligent Transportation Systems (ITS). A novel framework for detecting and mitigating low-rate DDoS attacks in ITS based on nonparametric statistical anomaly detection is proposed. Dealing with low-rate DDoS attacks is challenging since they can bypass traditional data filtering techniques while threatening the RSU availability due to their highly distributed nature. Extensive simulation results are presented for a real road scenario with the help of the SUMO traffic simulation software. The results show that our proposed method significantly outperforms two parametric methods for timely detection based on the Cumulative Sum (CUSUM) test, as well as the traditional data filtering approach in terms of average detection delay and false alarm rate.