Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments

Over the last few years, Software Defined Networking (SDN) paradigm has become an emerging architecture to design future networks and to meet new application demands. SDN provides resources for improving network control and management by separating control and data plane, and the logical control is centralized in a controller. However, the centralized control logic can be an ideal target for malicious attacks, mainly Distributed Denial of Service (DDoS) attacks. Recently, Deep Learning has become a powerful technique applied in cybersecurity, and many Network Intrusion Detection (NIDS) have been proposed in recent researches. Some studies have indicated that deep neural networks are sensitive in detecting adversarial attacks. Adversarial attacks are instances with certain perturbations that cause deep neural networks to misclassify. In this paper, we proposed a detection and defense system based on Adversarial training in SDN, which uses Generative Adversarial Network (GAN) framework for detecting DDoS attacks and applies adversarial training to make the system less sensitive to adversarial attacks. The proposed system includes well-defined modules that enable continuous traffic monitoring using IP flow analysis, enabling the anomaly detection system to act in near-real-time. We conducted the experiments on two distinct scenarios, with emulated data and the public dataset CICDDoS 2019. Experimental results demonstrated that the system efficiently detected up-to-date common types of DDoS attacks compared to other approaches. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

SDN is an emerging architecture for future networks, but centralized control logic can be vulnerable to DDoS attacks. This study proposes a detection and defense system based on Adversarial training in SDN, effectively detecting and mitigating DDoS attacks.