Software-defined networkings (SDNs) have grown rapidly in recent years be-cause of SDNs are widely used in managing large area networks and securing networks from Distributed Denial of Services (DDoS) attacks. SDNs allow net-works to be monitored and managed through centralized controller. Therefore, SDN controllers are considered as the brain of networks and are considerably vulnerable to DDoS attacks. Thus, SDN controller suffer from several challenges that exhaust network resources. For SDN controller, the main target of DDoS attacks is to prevent legitimate users from using a network resource or receiving their services. Nevertheless, some approaches have been proposed to detect DDoS attacks through the examination of the traffic behavior of networks. How-ever, these approaches take too long to process all incoming packets, thereby leading to high bandwidth consumption and delays in the detection of DDoS at-tacks. In addition, most existing approaches for the detection of DDoS attacks suffer from high positive/negative false rates and low detection accuracy. This study proposes a new approach to detecting DDoS attacks. The approach is called the statistical-based approach for detecting DDoS against the controllers of software-defined networks. The proposed approach is designed to detect the presence of DDoS attacks accurately, reduce false positive/negative flow rates, and minimize the complexity of targeting SDN controllers according to a statistical analysis of packet features. The proposed approach passively captures net-work traffic, filters traffic, and selects the most significant features that contribute to DDoS attack detection. The general stages of the proposed approach are (i) da-ta preprocessing, (ii) statistical analysis, (iii) correlation identification between two vectors, and (iv) rule-based DDoS detection.
SOFT COMPUTING BASED AUTONOMOUS LOW RATE DDOS
ATTACK DETECTION AND SECURITY FOR CLOUD COMPUTING