الفهرس 
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Abstract
”The Internet of Things (IoT) is a smart network that links all devices to the internet to exchange data via pre-determined protocols. The increasing use of IoT applications in various aspects of our lives has created a huge amount of data. IoT applications often require the presence of many technologies such as cloud computing and fog computing, which have led to serious challenges to security. One of the main challenges in network communications is an intrusion because of increasing Threat attacks. As a result of the use of these technologies, cyberattacks are also on the rise because current security methods are ineffective. Several Artificial Intelligence (AI)-based security solutions have been presented in recent years, including Intrusion Detection Systems (IDS). Feature selection (FS) approaches are required for the development of intelligent analytic tools that need data pretreatment and machine learning algorithm-performance enhancement. IDS is the process of recognizing attacks on information systems. Intrusions try to gain unapproved access to a computer system through these actions. FS approaches use by reducing the number of selected features, FS aims to improve classification accuracy. This thesis presents a new FS method through boosting the performance of Gorilla Troops Optimizer (GTO) based on the algorithm for Bird Swarms (BSA). This BSA is used to boost the performance exploitation of GTO in the newly developed GTO-BSA because it has a strong ability to find feasible regions with optimal solutions. As a result, the quality of the final output will increase, improving convergence. GTO-BSA’s performance was evaluated using a variety of performance measures on four IoT-IDS datasets: NSL-KDD, CICIDS-2017, UNSW-NB15 and BoT-IoT. The results were compared to those of the original GTO, BSA, and several state-of-the-art techniques in the literature. It has achieved an accuracy of 95.5%,98.7%,81,5%,81.5%, and in the NSL-KDD, CICIDS-2017, UNSW-NB15 and BoT-IoT datasets, respectively. According to the findings of the experiments, GTO-BSA had a better convergence rate and higher-quality solutions.