A novel framework for intrusion detection in IOT networks using hybrid optimization algorithm and convolutional neural networks

Due to extremely unpredictable and diverse network traffic, the rapid expansion of IoT devices has increased security risks. Conventional IDS models frequently fall short of maintaining high accuracy when dealing with unbalanced datasets and changing attack types. In order to increase the effectiveness and precision of network intrusion detection, this research presents an intrusion detection system for IoT networks. The proposed system makes use of a hybrid whale optimization algorithm-particle swarm optimization (WOA-PSO) for feature selection and convolutional neural networks (CNN) for network traffic classification. Robust and accurate attack classification is ensured by this hybrid optimization, which facilitates effective feature space search and exploitation. According to the comparative analysis, the suggested method obtains a much lower false alarm rate (FAR), a greater accuracy of 98.7 %, a precision of 99.3 %, and a recall of 98.3 %. These results demonstrate that the suggested approach is a trustworthy solution for IoT network security as it successfully improves intrusion detection, lowers false alarms, and guarantees a higher detection rate.