Detecting and Identifying the Targets of Covert DDoS Attacks
21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024, Doha, Qatar, 3 - 05 December 2024, pp.143-148, (Full Text)
- Publication Type: Conference Paper / Full Text
- Doi Number: 10.1109/honet63146.2024.10822944
- City: Doha
- Country: Qatar
- Page Numbers: pp.143-148
- Kocaeli University Affiliated: Yes
Abstract
Network systems are essential to our daily lives but remain vulnerable to Distributed Denial-of-Service (DDoS) attacks, particularly stealthy low-rate variants that evade conventional detection methods. This paper presents an SDN-based deep learning framework designed to detect adaptive low-rate DDoS attacks targeting both end hosts and network links. Our approach not only mitigates these threats but also differentiates between host-Targeted and link-Targeted attacks, effectively countering dynamic adversaries. We construct dataset of benign and low-rate DDoS traffic and evaluate our solution in an SDN environment using the Mininet emulator and RYU controller, demonstrating its efficacy in identifying and countering sophisticated low-rate attacks.