A network-based security information system for safeguarding computer-based test platforms in organizational environments
Computer-based testing (CBT) platforms have transformed education and certification by enabling scalable, efficient, and accessible examinations. However, these systems face significant cybersecurity risks, including unauthorized access, denial-of-service (DoS) attacks, and digital cheating, which threaten fairness and reliability. This study proposes a network-based security information system (NBSIS) designed specifically for CBT environments. The framework integrates layered defense, including pfSense firewalls (FW), Snort intrusion detection, Splunk security information and event management (SIEM), and artificial intelligence (AI)-powered analytics, into a unified architecture. A human-centered dashboard ensures usability for non-technical exam administrators, providing real-time alerts and intuitive controls. Validation through simulated attack scenarios demonstrated strong resilience, with high detection accuracy, reduced false positives, and rapid response times. Comparative analysis against intrusion detection system (IDS)-only and SIEM-only systems confirmed superior performance. The findings highlight NBSIS as a robust, scalable, and adaptive solution that safeguards exam integrity while remaining practical for diverse organizational contexts. This research contributes to computer science by advancing secure architecture, applying AI-driven anomaly detection, and integrating human-computer interaction principles into cybersecurity for education.