Security Challenges and Solutions in the Development of a Website for Administering a Virtual University

In today’s education landscape, the rise of universities highlights the crucial need for strong security measures to protect valuable educational assets and student information. This study delves into the security obstacles and strategies involved in creating a website for managing a university. Our main goal is to bolster website security by incorporating machine learning algorithms. Building on established research we offer an overview of universities. Emphasize the pivotal role of website security. Using the Kaggle Network Traffic Dataset we introduce a framework that includes data gathering, feature development, model selection and training assessment criteria, and implementation of machine learning techniques. By utilizing algorithms like Isolation Forest, One Class SVM, and K Means Clustering our system achieves a 98% accuracy rate with false positives and negatives. Our research highlights the significance of surveillance and adaptation to evolving threats while setting a foundation for exploration into refining models exploring advanced methods and tackling scalability issues. Ultimately our study contributes to discussions on website security in virtual learning environments by paving the way for progress in this field.