A novel bidirectional LSTM model for network intrusion detection in SDN-IoT network

Abstract

The advancement of technology allows for easy adaptability with IoT devices. Internet of Things (IoT) devices can interact without human intervention, which leads to the creation of smart cities. Nevertheless, security concerns persist within IoT networks. To address this, Software Defined Networking (SDN) has been introduced as a centrally controlled network that can solve security issues in IoT devices. Although there is a security concern with integrating SDN and IoT, it specifically targets Distributed Denial of Service (DDoS) attacks. These attacks focus on the network controller since it is centrally controlled. Real-time, high-performance, and precise solutions are necessary to tackle this issue effectively. In recent years, there has been a growing interest in using intelligent deep learning techniques in Network Intrusion Detection Systems (NIDS) through a Software-Defined IoT network (SDN-IoT). The concept of a Wireless Network Intrusion Detection System (WNIDS) aims to create an SDN controller that efficiently monitors and manages smart IoT devices. The proposed WNIDS method analyzes the CSE-CIC-IDS2018 and SDN-IoT datasets to detect and categorize intrusions or attacks in the SDN-IoT network. Implementing a deep learning method called Bidirectional LSTM (BiLSTM)--based WNIDS model effectively detects intrusions in the SDN-IoT network. This model has achieved impressive accuracy rates of 99.97% and 99.96% for binary and multi-class classification using the CSE-CIC-IDS2018 dataset. Similarly, with the SDN-IoT dataset, the model has achieved 95.13% accuracy for binary classification and 92.90% accuracy for multi-class classification, showing superior performance in both datasets.