English

Abstract

The latest wireless sensor network (WSN) developments in critical applications have introduced security risks, like jamming. Intrusion Detection System (IDS) in WSN is the method of recognizing malevolent or unauthorized activities in the network. The intruder's presence to launch different attacks within the network cannot be disregarded. Despite a great deal of effort by the researcher workers, IDS still experienced difficulties enhancing recognition performance while minimizing the false alarm rate and identifying novel intrusions. Recently, Deep Learning (DL) and Machine Learning (ML) based IDS system has been deployed as promising solution to effectively identify intrusion across the network. Therefore, the study presents a Moth Search Optimization with DL-based Intrusion Detection (MSODL-ID) method in the WSN. The MSODL-ID technique aims to effectually identify the occurrence of malicious activities or intrusions in the network. To accomplish this, the MSODL-ID technique undergoes two stages of preprocessing: data conversion and data scaling. In addition, the MSODL-ID technique employs Convolutional Recurrent Neural Network (CRNN) model with a Hopfield layer for intrusion detection purposes. For optimal hyperparameter selection of the CRNN model, the MSO algorithm is used and thereby enhances the classification performance of the CRNN model. The stimulation analysis of the MSODL-ID system is tested by means of Kaggle datasets, and the outcomes exhibit the promising performance of the MSODL-ID system over other current DL approaches.