ddos attacks

traffic classification

network traffic

denial service

encrypted traffic

attack detection

network intrusion

deep packet inspection

Future Generation Computer Systems

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. • This work proposes a detection and defense system against adversarial DDoS attacks through an Adversarial Deep Learning approach. • The proposed system collects and analyzes the network traffic every second, enabling the anomaly detection system to act in near-real-time. • We conduct the experiments through up-to-date common DDoS attacks using two different datasets. • We compare the efficiency of the proposed system with different deep learning methods.

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