The rapid adoption of wireless networks in educational institutions has enhanced accessibility to digital resources but simultaneously exposed these networks to sophisticated cyber-physical threats. Traditional intrusion detection systems (IDS) are inadequate in addressing the dynamic nature of modern attacks, necessitating the development of advanced frameworks that integrate anomaly detection and machine learning. This paper proposes and implements a modular intrusion detection framework tailored for wireless campus networks, with a case study on the National Institute of Construction Technology and Management (NICTM), Uromi. The framework encompasses data collection, feature extraction, detection, incident management, and visualization modules. It integrates rule-based methods with placeholders for supervised machine learning models, ensuring scalability and adaptability. The architecture leverages PHP, MySQL, and Bootstrap for proof-of-concept deployment, alongside provisions for future integration with advanced algorithms and real-time streaming via WebSockets. The study anticipates enhanced detection accuracy, reduced false positives, and comprehensive monitoring capabilities. The findings from this research contribute both theoretical insights and practical applications, providing a foundation for future intrusion detection systems in academic and institutional environments.