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Beyond Firewall: Leveraging Machine Learning for Real-Time Insider Threats Identification and User Profiling

Author

Listed:
  • Saif Al-Dean Qawasmeh

    (Department of Applied Science and Technology, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA)

  • Ali Abdullah S. AlQahtani

    (Department of Software Engineering (Cybersecurity Track), Prince Sultan University, Riyadh 12435, Saudi Arabia)

Abstract

Insider threats pose a significant challenge to organizational cybersecurity, often leading to catastrophic financial and reputational damages. Traditional tools such as firewalls and antivirus systems lack the sophistication needed to detect and mitigate these threats in real time. This paper introduces a machine learning-based system that integrates real-time anomaly detection with dynamic user profiling, enabling the classification of employees into categories of low, medium, and high risk. The system was validated using a synthetic dataset, achieving exceptional accuracy across machine learning models, with XGBoost emerging as the most effective.

Suggested Citation

  • Saif Al-Dean Qawasmeh & Ali Abdullah S. AlQahtani, 2025. "Beyond Firewall: Leveraging Machine Learning for Real-Time Insider Threats Identification and User Profiling," Future Internet, MDPI, vol. 17(2), pages 1-26, February.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:2:p:93-:d:1593339
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    References listed on IDEAS

    as
    1. David Rios Insua & Aitor Couce‐Vieira & Jose A. Rubio & Wolter Pieters & Katsiaryna Labunets & Daniel G. Rasines, 2021. "An Adversarial Risk Analysis Framework for Cybersecurity," Risk Analysis, John Wiley & Sons, vol. 41(1), pages 16-36, January.
    2. Duc C. Le & Nur Zincir‐Heywood, 2021. "Exploring anomalous behaviour detection and classification for insider threat identification," International Journal of Network Management, John Wiley & Sons, vol. 31(4), July.
    3. Mustafa Al Lail & Alejandro Garcia & Saul Olivo, 2023. "Machine Learning for Network Intrusion Detection—A Comparative Study," Future Internet, MDPI, vol. 15(7), pages 1-17, July.
    Full references (including those not matched with items on IDEAS)

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