<![CDATA[This study aims to develop a mechanism for detecting machine learning-based cyber intrusions in electronic learning systems. In today's digital era, e-learning systems have become an integral part of education and training, providing global accessibility and more interactive learning efficiency. However, security and privacy challenges are becoming critical issues due to the increasingly real threat of cyber intrusion. Attackers try to take advantage of vulnerabilities and weaknesses in e-learning systems to steal sensitive data or disrupt operations. To overcome this problem, this study focuses on the use of artificial intelligence technologies, especially machine learning, to proactively detect and respond to intrusive threats. Through e-learning security analysis, identification of weaknesses, and potential loopholes for cyber-attacks, the most suitable machine learning algorithms are selected to detect patterns and signs of intrusion attacks on network data. The evaluation results show that several machine learning algorithms, such as SVM and Decision Tree, have good performance in recognizing cyber intrusions with high accuracy, precision, recall, F1-score, and ROC-AUC. By implementing machine learning-based intrusion detection technology, it is expected that electronic learning systems can be more proactive in identifying and responding to intrusion threats before significant damage occurs. This research has significant benefits in increasing security and privacy in the use of electronic learning systems. In addition, this study is expected to be a reference for further research in the world of cyber security and the application of artificial intelligence technology in supporting digital security.]]>
