I. Introduction
In recent years, Intrusion Detection Systems (IDS) are often utilized to improve the security of device communication and detect network intrusions. These IDSs serve a critical role in ensuring secure internet connectivity and rapidly alerting system administrators when harmful actions are detected [1]. there has been a growing trend in the adoption of cloud computing (CC) in both the educational and commercial sectors. It is generally recognized for its utility in data storage and retrieval in cloud systems [2]. DL-based algorithms in particular confront issues such as high model complexity and big model size [3]. Efforts have been made to integrate cybersecurity systems with modern communication technologies, particularly in industries such as manufacturing, where monitoring and control capabilities for industrial systems are critical [4]. As wireless technology and equipment become more control, guaranteeing the security of wireless communication channels becomes increasingly important. These channels carry serious business data such as remote control, telemetry, and remote signaling, needing protection against malicious node activity and unauthorized access [5]-[6]. The limitation of energy resources, particularly in devices with low battery capacity, presents a substantial challenge to network performance. As a result, reducing energy consumption is a critical requirement for attaining quality of service (QoS) in the Internet of Things (IoT) setting [7]. The Internet of Things has the potential to boost production and efficiency in smart sectors through intelligent decision-making and remote management. However, the rapid expansion of IoT networks has generated uncertainties about security and privacy [8]. The major contributions are discussed as follows;
The preprocessing is done after collection data from BoT-IoT dataset which improves the model performance and fed to the feature selection.
The hybrid Grasshopper Optimization Algorithm - Whale Optimization Algorithm (GWOA) is utilized for the feature selection process and Faster Recurrent CNN (FR-CNN) is utilized for classification.
The proposed GWOA based on FRCNN model was evaluated by utilizing evaluation metrics like accuracy, precision, recall, specificity, and f1-score.