Lightweight Statistical Approach towards TCP SYN Flood DDoS Attack Detection and Mitigation in SDN Environment

Abstract

Distributed Denial of Service (DDoS) attack is known to be one of the most lethal attacks in traditional network architecture. In this attack, the attacker uses botnets to overwhelm network resources. Botnets can be randomly compromised computers or IoT devices that are used to generate excessive traffic towards the victim, and as a result, legitimate users cannot access the services. In this research, software-defined networking (SDN) has been suggested as a solution to fight DDoS attacks. SDN uses the idea of centralized control and segregation of the data plane from the control plane. SDN is more flexible, and policy implementation on the centralized controller is easy. SDN is now being widely used in modern network paradigms because it has enhanced security. In this work, an entropy-based statistical approach has been suggested to detect and mitigate TCP SYN flood DDoS attacks. The proposed algorithm uses a three-phased detection scheme to minimize the false-positive rate. Entropy, standard deviation, and weighted moving average have been used for intrusion detection. Multiple experiments were performed, and the results show that the suggested approach is more reliable and lightweight and has a minimal false-positive rate.