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Computer Science and Information Technologies
Published by Institute of Advanced Engineering and Science
ISSN : 2722323X     EISSN : 27223221     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Computer Science and Information Technologies ISSN 2722-323X, e-ISSN 2722-3221 is an open access, peer-reviewed international journal that publish original research article, review papers, short communications that will have an immediate impact on the ongoing research in all areas of Computer Science/Informatics, Electronics, Communication and Information Technologies. Papers for publication in the journal are selected through rigorous peer review, to ensure originality, timeliness, relevance, and readability. The journal is published four-monthly (March, July and November).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 191 Documents
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A network-based security information system for safeguarding computer-based test platforms in organizational environments Ajani Dele; Owolabi Abdulhakim Adewale; Inaya Adesuwa
Computer Science and Information Technologies Vol 7, No 3: November 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i3.p325-336

Abstract

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.

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