Class imbalance resolution in IoT networks using advanced elk herd optimization with SMOTE and iteratively fine-tuned deep BiLSTM

Abstract

The "internet of things (IoT)" mentions to a system in which multiple network protocols are used to link together disparate devices that are always sharing information with one another. The hope is that this study will add to the existing body of knowledge by suggesting ways to make intrusion detection systems (IDS) more effective. Training datasets for the proposed model were collected from telemetry of network (ToN)-IoT network traffic. After cleaning and normalizing the datasets, the synthetic minority over sampling technique (SMOTE) is used to balance the datasets that are imbalanced. Optimal sampling rates are critical for resolving class imbalance, as SMOTE's efficiency is dependent on it for instances involving minority classes. Improving classification accuracy through finding appropriate sample rates for input datasets is the goal of this paper's introduction of advanced elk herd optimization (AEHO) with SMOTE. Finally, a deep bidirectional long short-term memory (deep BiLSTM) model based on deep learning is used to classify attacks. The fine-tuning technique is used during testing to update the high limits and when combined with the data balancing mechanism and AEHO-based-SMOTE, the results greatly enhance the classification techniques. Deep BiLSTM performs better than 90% in every category: classification, recall, precision, and F1-score.