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Hasanain M. J. Alfouadi
University of Al-Qadisiyah
Computer Science & IT

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Novel Feature Selection Method for APT Detection Hasanain M. J. Alfouadi; Hiba Abdulrazzak Ahmed
Journal of Technology and System Information Vol. 3 No. 2 (2026): April
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i2.5931

Abstract

An Advanced Persistent Threat (APT) is a multistage, highly sophisticated, and covert form of cyber threat that gains unauthorized access to networks to either steal valuable data or disrupt the targeted network. These threats often remain undetected for extended periods, emphasizing the critical need for early detection in networks to mitigate potential APT consequences. In this work, we propose a feature selection method for developing a lightweight intrusion detection system capable of effectively identifying APTs at the initial compromise stage. Our approach leverages the XGBoost algorithm and Explainable Artificial Intelligence (XAI), specifically utilizing the SHAP (SHapley Additive exPlanations) method for identifying the most relevant features of the initial compromise stage. The results of our proposed method showed the ability to reduce the selected features of the SCVIC-APT-2021, dataset, a benchmark dataset for APT detection that contains both benign and malicious traffic records, from 77 to just four while maintaining consistent evaluation metrics for the suggested system. The estimated metrics values are 97% precision, 100% recall, and a 98% F1 score. The proposed method not only aids in preventing successful APT consequences but also enhances understanding of APT behavior at early stages.
Co-Authors Hiba Abdulrazzak Ahmed