Networks Cyber Security Model by Using Machine Learning Techniques - Obaid Sari, Farah Abbas; Habib Alrammahi, Ali Abdulkarem; Hameed, Asaad Shakir; B. Alrikabi, Haiffa Muhsan; Abdul–Razaq, Abeer A.; Nasser, Huda Karem; AL-Rifaie, Mohammed F. (International Journal of Intelligent Systems and Applications in Engineering, 2022)
Since artificial intelligence relies on learning just like humans, it is useful to use these algorithms to address cyber-attacks, which represent the greatest concerns for network users, especially companies and institutions, as a result of the dire consequences of these attacks such as large material losses and the leakage or falsification of important data. The methods used to detect cyber-attacks are slow or they detect attacks after their occurrence and then analyze them and issue reports. In this research, we propose a conceptual framework that contains new rules that are used along with the previous rules for the purpose of creating a network monitoring tool in order to counteract cyber-attacks in real-time by relying on artificial intelligence algorithms such as classification and prediction based on user behavior. GMM algorithm has been suggested in this paper because of its efficiency in comparison with the commonly used algorithms in this sector like k-means it depends on behavioral similarities, not on distance.