aspg-2-1861-1791417397 20261007235637 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Cybersecurity and Information Management JCIM 2769-7851 2690-6775 2023 12 1 Mustafa El-Taie Digital Charging Solutions GmbH, Germany Aaras Y.Kraidi University of Technology and Applied Science, Shinas, Oman The use of machine learning methods in cybersecurity is only one of many examples of how this once-emerging innovation has entered the mainstream. Anomaly-based identification of common assaults on vital infrastructures is only one instance of the various applications of malware analysis. Scholars are using machine learning-based identification in numerous cybersecurity solutions since signature-based approaches are inadequate at identifying zero-day threats or even modest modifications of established assaults. In this work, we introduce the machine-learning models-based security framework to detect cyber-attacks. This paper used three machine learning models Logistic Regression, Random Forest, and K-Nearest Neighbor This framework not only reduces the computational difficulty of the framework by minimizing the feature parameters, but it also performs well in terms of accuracy in forecasting unknown scenarios in the tests. Finally, we ran trials using cybersecurity datasets to measure the machine learning model's performance using metrics including precision, recall, and accuracy. 2023 41 49 1861 https://creativecommons.org/licenses/by/4.0/ 10.54216/JCIM.120104 https://www.americaspg.com/journal/2/article/1861 https://www.americaspg.com/storage/81686782866.pdf