Development of Network Intrusion Detection and Alert System: a Case Study of Bamidele Olumilua University of Education, Science and Technology, Ikere-Ekiti

ABSTRACT This project focuses on the development of a network intrusion detection and alert system for suspicious activity on the Bouesti portal. The system is designed to detect and prevent various types of cyber threats, including malware, phishing, and denial-of-service (DoS) attacks. The system uses a combination of machine learning and deep learning algorithms to analyze network traffic data and identify patterns of suspicious activity. The algorithms are trained on a dataset of labeled network traffic data, which includes both benign and malicious traffic. Once suspicious activity is detected, the system sends real-time alerts to administrators and security personnel, providing them with detailed information about the detected threat. The system also provides recommendations for mitigating the threat and preventing future attacks. The system is designed to be scalable and flexible, allowing it to be easily integrated with existing security systems and tools. The system is also designed to be user-friendly, providing administrators and security personnel with an intuitive interface for configuring and managing the system. The project involves the development of a prototype system, which is tested and evaluated using a dataset of network traffic data collected from the Bouesti portal. The results of the evaluation show that the system is effective in detecting suspicious activity and providing real-time alerts. The system has the potential to significantly improve the security of the Bouesti portal, protecting against various types of cyber threats and preventing financial losses and reputational damage. The system can also be used as a model for developing similar systems for other organizations and industries. Overall, this project demonstrates the feasibility and effectiveness of using machine learning and deep learning algorithms for network intrusion detection and alerting. The project also highlights the importance of developing robust security systems for protecting against cyber threats.