🤖 AI Summary
This work proposes an end-to-end trustworthy detection framework to address three major challenges in cybersecurity threat detection: large-scale data volume, high risk of feature leakage, and opaque model decisions. The framework uniquely integrates strategic sampling—preserving class distribution to enhance training efficiency—an automated data leakage prevention mechanism, and model-agnostic SHAP-based interpretability analysis. Experimental evaluation on the CIC-IDS2017 dataset demonstrates that the proposed approach significantly reduces computational overhead while maintaining high detection performance. Furthermore, it delivers actionable explanations for security analysts, thereby facilitating the practical deployment of trustworthy AI in Security Operations Centers (SOCs).
📝 Abstract
The critical need for transparent and trustworthy machine learning in cybersecurity operations drives the development of this integrated Explainable AI (XAI) framework. Our methodology addresses three fundamental challenges in deploying AI for threat detection: handling massive datasets through Strategic Sampling Methodology that preserves class distributions while enabling efficient model development; ensuring experimental rigor via Automated Data Leakage Prevention that systematically identifies and removes contaminated features; and providing operational transparency through Integrated XAI Implementation using SHAP analysis for model-agnostic interpretability across algorithms. Applied to the CIC-IDS2017 dataset, our approach maintains detection efficacy while reducing computational overhead and delivering actionable explanations for security analysts. The framework demonstrates that explainability, computational efficiency, and experimental integrity can be simultaneously achieved, providing a robust foundation for deploying trustworthy AI systems in security operations centers where decision transparency is paramount.