OptFBFN: IOT threat mitigation in software-defined networks based on fuzzy approach

Abstract

Software-Defined Networking (SDN) has emerged as a new architectural paradigm in computer networks, aiming to enhance network capabilities and address the limitations of conventional networks. Despite its many advantages, SDN has encountered numerous attack risks and vulnerabilities. Using an intrusion detection system (IDS) is one of the most important ways to address threats and concerns in the SDN. The great flexibility, adaptability, and programmability of SDN, together with other unique qualities, make the integration of IDS into the SDN network effective. The majority of these methods are less scalable and have poor accuracy. This research suggests an Optimized Fuzzy Based Function Network (OFBFN) to solve this problem. The Modified ResNet152 method is utilized to extract features from the input data. The Binary Waterwheel Plant Algorithm (BWWPA) selects the essential features. To characterize attacks within the InSDN, BOT-IOT, ToN-IoT, and CICIDS 2019 datasets, the system first selects the most efficient features. Then, it employs the FBFN with the Coatis Optimization Algorithm for classification. The suggested system classifies attacks and benign traffic, distinguishes between different types of attacks, and specifies high-performance sub-attacks. Four benchmark datasets were utilized for training and evaluating the proposed system, demonstrating its effectiveness. According to the findings from the experiments, the suggested approach performs better than others at identifying a wide range of threats.