Dual Feature-Based Intrusion Detection System for IoT Network Security

Abstract

The Internet of Things (IoT) has enabled widespread connectivity of smart devices but remains susceptible to cyber intrusions. In this research, a novel dual feature optimization using deep learning network for intrusion Detection (FOUND) technique has been proposed for enhancing security in IoT environments. The proposed method utilizes the bald eagle search (BES) algorithm and butterfly optimization algorithm (BOA) to capture both flow and packet level features to enhance the accuracy of the intrusion detection process. Moreover, a multi-head attention-based bidirectional gated recurrent unit (MHA-BiGRU) is utilized to classify Attack and Non-Attack classes with high precision. The efficacy of the suggested approach is measured utilizing metrics including recall (RC), accuracy, precision (PR), and f1score (F1S). Experimental outcomes utilizing BoT-IoT and UNSW-NB15 datasets demonstrate greater accuracy over existing models. In BoT-IoT, the accuracy of the FOUND approach is 1.5%, 1.1%, and 2.5% increase compared to existing GRU, RNN, and GCN methods, respectively.