Entropy-based DDoS Attack Detection in SDN using Dynamic Threshold

• Published in: 2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS) pp. 1 - 5
• Main Authors: Hemmati, Zahra, Mirjalily, Ghasem, Mohtajollah, Zahra
• Format: Conference Proceeding
• Language: English
• Published: IEEE 29.12.2021
• Subjects: Controller; Deep learning; Denial-of-service attack; Distributed Denial of Service Attacks; Dynamic Threshold; Entropy; Feature extraction; Heuristic algorithms; Signal processing; Signal processing algorithms; Software Defined Networks
• DOI: 10.1109/ICSPIS54653.2021.9729355

The centralized structure of software defined networks makes them vulnerable to distributed denial of service attacks. Given that these attacks can easily destroy the computational and communicational resources of controller and switches, they make the network fail in a short time. Hence, it is vital to protect the controller. Utilizing the unique features of software defined networks, this paper propounds an effective method to detect distributed denial of services attacks. For this purpose, entropy was used to detect attacks. Furthermore, this method utilizes a dynamic threshold instead of a static one to distinguish between normal and attack traffic. The dynamic threshold heightens the accuracy of attack detection in the proposed algorithm to 98% on average while the accuracy in the benchmark algorithm using entropy and the static threshold is 96%.