Hybrid Model Analysis for Intrusion Detection in IoT Applications: A Novel Approach

This article develops a novel political optimizer with cascade forward neural network (PO-CFNN-)-based IDS in the IoT environment. The major intention of the PO-CFNN technique is to define the occurrence of intrusions in the IoT environment. Networks for the Internet of Things (IoT) have recently grown in importance for applications such as smart cities, smart buildings, health care, and others. Because IoT devices tend to be inexpensive, small, and low-powered, it finds them to be advantageous. In the design of IoT networks, security continues to be a difficult problem. Network intrusions, or irregular activities in the network, can be detected using intrusion detection systems (IDS). The latest advances in machine learning (ML) and metaheuristics can be employed to design effective IDS models for IoT networks. This article develops a novel political optimizer with cascade forward neural network (PO-CFNN-)-based IDS in the IoT environment. The PO-CFNN technique's main goal is to identify instances of intrusions from the IoT environment. The three main steps of the PO-CFNN technique are preprocessing, classification, and parameter optimisation. The networking data is first preprocessed to put it in a format that can be used. Following that, the CFNN technique is employed for the identification and classification of intrusions in the IoT environment. In the final stage, the PO algorithm is applied for the optimal adjustment of the parameters involved in the CFNN model. The experimental validation of the PO-CFNN technique on a benchmark dataset stated the better outcomes of the PO-CFNN technique over recent approaches.