Two-Tiered Privacy Preserving Framework for Software-Defined Networking Driven Defence Mechanism for Consumer Platforms

Software-defined networking (SDN) is a novel network theory that divides the controller from the network devices such as switches and routers. The integrated SDN structure enables the global network organization and tackles the necessity of present data centers. There are great advantages presented by the architecture of SDN, the hazard of novel assaults is a vital issue and can avert the widespread acceptance of SDNs. The controller of SDN is an essential part, and it is a tempting aim for the invaders. If the attacker effectively acquires the SDN controller, it can transmit the traffic depending upon its desires, causing severe loss to the complete system. Mobile users can access crucial actual services over wireless models such as software-defined networks (SDNs) topologies and the Internet of Things (IoTs). Thus, managing power consumption and system and device congestion turns into a main problem for SDN-based IoT applications. Network intrusion detection systems (NIDSs) are significant devices for identifying and protecting the network landscape from anomalous attacks and malicious activity. Currently, deep Learning (DL) has revealed desired outcomes in a diversity of problems like speech, image, text applications, etc. Whereas numerous works used DL for NIDSs, almost all these methods neglect the outcome of the overfitting issue throughout the execution of DL techniques. This study presents a novel Enhancing Software-Defined Networking Security with Deep Learning and Hybrid Feature Selection (ESDNS-DLHFS) technique for consumer platforms. The proposed ESDNS-DLHFS system primarily focuses on protecting data privacy in SDN-assisted IoT platforms. In the ESDNS-DLHFS method, the initial phase of min-max normalization is executed to scale the input data. For the feature selection process, the hybrid crow search arithmetic optimization algorithm (HCSAOA) is utilized to optimally select feature subsets. Next, the deep bidirection- al long short-term memory (Deep BiLSTM) technique is applied to detect intrusions. Finally, the enhanced artificial orca's algorithm (EAOA) based hyperparameter tuning process is executed to increase the overall classification outcomes. To certify the improved predictive outcomes of the ESDNS-DLHFS technique, an extensive range of experiments are implemented on the benchmark dataset. The comparison outcome study shows the promising performance of the ESDNS-DLHFS technique compared to the recent approaches.