Optimization-assisted deep two-layer framework for ddos attack detection and proposed mitigation in software defined network

Security has become crucial as Internet of Things (IoT) applications proliferate. IoT vulnerabilities are widespread, as demonstrated by a recent distributed denial-of-service (DDoS) assault, which many IoT devices unintentionally assisted with. IoT device management may be done safely with the help of the new software-defined anything (SDx) paradigm. In this study, a five-phase SDN design will be equipped with a detection and mitigation system of DDoS attack. Data cleaning is a method of pre-processing raw data that is crucial to the flow of information. The suitable features are chosen from the retrieved features using the augmented chi-square method. A deep two-layer architecture with four classifiers is utilized to characterize the attack's detection stage. Using the recently created hybrid optimization method known as the MUAE approach, the weight of the QNN is adjusted. Until the optimized QNN detects an attacker, regular data routing occurs. In that scenario, control is passed along to the mitigation of attacks step. For training rates of 60, 70, 80, and 90, the predicted accuracy of the model is 94.273%, 94.860%, 94.93%, and 96.02%. Finally, the decided system is verified against traditional ways to demonstrate its superiority in both mitigation and attack detection.