Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/3135 2019 2019 Department of Computer Engineering Techniques, College of Engineering, University of Al Maarif, Al Anbar, 31001, Iraq Mohammed Mohammed Department of Computer Engineering Techniques, College of Engineering, University of Al Maarif, Al Anbar, 31001, Iraq Khudhair Abed Thamer Department of Computer Engineering Techniques, College of Engineering, University of Al Maarif, Al Anbar, 31001, Iraq Ahmed Hikmat Saeed Department of Computer Engineering Techniques, College of Engineering, University of Al Maarif, Al Anbar, 31001, Iraq Mohammed Yousif Middle Technical University, Baghdad, Iraq Ahmad Salim Renewable Energy Research Center, University of Anbar, Ramadi, Iraq Qusay Hatem Alsultan Al-Huda University College, Ramadi, Iraq Salim Bader Currently, building a high-performance attack detector for cyber threat should be an essential and challenging task to secure cloud system from malicious activities. Traditional methodologies have become subject to the challenge of overfitting, distributive and intricate system layout, comprehensibility and more extended time particles. Therefore, the proposed contribution can be an efficient solution to design and develop a secure system, which is able to recognize cyber threats from cloud systems. It includes preprocessing and normalization, feature extraction, optimization as well prediction modules. Normalization with the relevant per batch fast Independent Component Analysis (ICA) model. A Genetic Algorithm (GA) - Gray Wolf Optimization (GWO) is then used to select the discriminatory features for training and testing phases. In the end, GAGWO- Random Forest (RF) is employed to classify the flow of data as insider or outsider. The detection system is implemented by taking popular and publicly available datasets like BoT-IoT, KDD Cup’99 etc. The various percentage indicators of feasibility are used as a validation purpose like detection accuracy measuring and comparing with the suggested GAGWO-RF system. Overall Accuracy: The proposed GAGWO-RF system achieved an average accuracy rate at 99.8% on all datasets the used. From the performance study, we have noted that GAGWO-RF security model performs better than other models. 2025 2025 225 232 10.54216/JCIM.150117 https://www.americaspg.com/articleinfo/2/show/3135