Efficient and Secure Architecture for Mitigating DDoS Attacks in Software-Defined Vehicular Networks

Abstract

Vehicular Ad Hoc Networks (VANETs) and Software-Defined Networking (SDN) have been combined to create SDN-enabled VANET architectures, which provide Intelligent Transportation Systems (ITS) with enhanced resource management, centralized control, and flexibility. However, the centralized control structure introduces new security challenges, notably Distributed Denial of Service (DDoS) attacks, which can significantly impact network stability and availability. In this study, we propose a DDoS detection framework for SDN-enabled VANETs, leveraging the Snort Intrusion Detection System (IDS) to effectively identify and mitigate DDoS threats. Our approach integrates Snort IDS into an SDN architecture based on the RYU controller, where Snort monitors network traffic for anomalies, and the SDN controller enforces dynamic mitigation strategies. The system is implemented using Mininet-WiFi, simulating a vehicular network environment with 10 vehicles nodes and a single RYU SDN controller. Performance evaluation under DDoS attack scenarios is conducted using the iPerf tool to measure key network metrics, including throughput, jitter, and packet loss rate. The results demonstrate that Snort IDS significantly improves network performance: jitter is reduced by up to 35%, packet loss rate decreases from over 40% to 15–25%, and throughput improves from 5–7 Mbps to a stable 10 Mbps. This study also explores the design and deployment of Snort within a Software-Defined Vehicular Network (SDVN) environment for effective DDoS detection and mitigation. By highlighting the importance of robust security mechanisms in SDN-enabled VANET architectures, this work contributes to the development of secure and reliable ITS infrastructures.