Hybrid Grasshopper Whale Optimization Algorithm Based on Faster Recurrent Convolutional Neural Network for Intrusion Detection System in Internet of Things

Intrusion Detection System (IDS) is one of the general Deep Learning (DL) techniques which are utilized to find and identify the outliers to prevent adversarial attacks, fraud, intrusions in Internet of Things (IoT). The proposed hybrid optimization technique known as GWOA by integrating the Grasshopper Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA). For better IDS performance, this GWOA can be utilized into the Faster Recurrent Convolutional Neural Network (FRCNN) model. This proposed method utilized the BoT-IoT dataset to assess the efficacy of this GWOA-based strategy. The main objective was to improve the IDS's balance between exploration and exploitation. This system makes use of the FRCNN to categorize incursions precisely and effectively, improving the overall detection capabilities. When compared to measures of evaluation like precision, recall, accuracy, and f1-score, the suggested GWOA-based FRCNN model's results show a considerable improvement. Comparing the model to other intrusion detection methods, such as Deep Neural Network (DNN), Long Short-Term Memory (LSTM) and IDS-SIoEL. It attains values of 93.77% for accuracy, 86.66% for precision, 95.87% for recall, and 91.03% for f1-score.