Problem Statement
Distributed Denial of Service (DDoS) attacks pose a significant threat to online
services by overwhelming servers with traffic. Traditional mitigation techniques
may not respond quickly enough to prevent service disruptions, necessitating a
real-time detection and mitigation system.
Abstract
This project focuses on developing a real-time detection and mitigation system
for DDoS attacks. The system will employ AI algorithms to analyze network traffic
patterns, detect anomalies indicative of DDoS attacks, and implement automated
mitigation measures to ensure minimal disruption to online services.
Outcome
A robust system for real-time detection and mitigation of DDoS attacks,
enhancing the security and availability of online services.
Reference
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.
- J. Zhang, F.-Y. Wang, K. Wang, W.-H. Lin, X. Xu, and C. Chen, “Data-driven intelligent transportation systems: A survey,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 4, pp. 1624-1639, 2011.
- C. Ponikwar and H.-J. Hof, “Overview on security approaches in intelligent transportation systems,” 2015.
- F. Sakiz and S. Sen, “A survey of attacks and detection mechanisms on intelligent transportation systems: Vanets and iov,” Ad Hoc Networks, vol. 61, pp. 33-50, 2017.
- G. Samara, W.A. Al-Salihy, and R. Sures, “Security analysis of vehicular ad hoc networks (vanet),” Network Applications Protocols and Services (NETAPPS) 2010 Second International Conference on, pp. 55-60, 2010.
- C. Zhang, Z. Cai, W. Chen, X. Luo, and J. Yin, “Flow level detection and filtering of low-rate ddos,” Computer Networks, vol. 56, no. 15, pp. 3417-3431, 2012.
https://ieeexplore.ieee.org/document/8569698/