Enhancing Collaborative Mitigation of Volumetric Ddos Attacks and Failure in Multi-sdn Networks
Year: 2024
Type: article
Abstract: Multi-SDN (Software-Defined Networking) architectures offer enhanced flexibility and control over network traffic, which is essential in mitigating Distributed Denial of Service (DDoS) attacks. Volumetric DDoS attacks, characterized by overwhelming network traffic, pose significant threats to these networks, leading to severe service disruptions and failures. Despite the potential of SDN to manage network traffic effectively, the collaborative mitigation of volumetric DDoS attacks remains challenging. Existing solutions often lack coordination among multiple SDN controllers, resulting in inefficient resource utilization and delayed response times. This study proposes an enhanced collaborative mitigation strategy that leverages inter-controller communication and resource sharing in Multi-SDN environments. The method involves three key components: (1) a real-time detection algorithm using machine learning to identify DDoS traffic, (2) a dynamic resource allocation mechanism to distribute mitigation tasks among controllers, and (3) an inter-controller communication protocol to ensure coordinated responses. The detection algorithm was trained on a dataset of network traffic, achieving a 95% accuracy rate. The resource allocation mechanism optimizes the distribution of mitigation tasks, reducing response times by 30%. The proposed method was evaluated in a simulated Multi-SDN environment under various volumetric DDoS attack scenarios. The results demonstrated a significant improvement in mitigation effectiveness. The attack detection accuracy reached 94.5%, and the coordinated mitigation reduced average response times by 28%. Network throughput and latency were improved by 25% and 22%, respectively, compared to traditional non-collaborative methods.
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4403501142