Human-Centered Explainable AI for Security Enhancement: A Deep Intrusion Detection Framework
#Explainable AI #XAI #Intrusion Detection System #IDS #NSL‑KDD #Deep Learning #Cyber Threats #Transparency #Interpretability #Benchmark Dataset #arXiv Preprint
📌 Key Takeaways
- New IDS framework announced on arXiv (Feb 2026).
- Incorporates Explainable AI to improve model transparency.
- Designed to balance high detection accuracy with interpretability.
- Evaluated using the NSL‑KDD benchmark dataset.
- Shows better performance than conventional black‑box IDS systems.
📖 Full Retelling
In February 2026, the authors of a preprint on arXiv announced a novel intrusion detection system (IDS) framework that integrates explainable artificial intelligence (XAI). The new design links deep learning models to XAI techniques, aiming to deliver both high detection accuracy and clear interpretability in the face of increasingly complex and frequent cyber‑threats. By evaluating the system on the benchmark NSL‑KDD dataset, the study demonstrates superior performance relative to traditional black‑box IDS approaches, highlighting a practical method for enhancing security transparency.
🏷️ Themes
Explainable Artificial Intelligence, Intrusion Detection, Cybersecurity, Deep Learning, Model Transparency
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Original Source
arXiv:2602.13271v1 Announce Type: new
Abstract: The increasing complexity and frequency of cyber-threats demand intrusion detection systems (IDS) that are not only accurate but also interpretable. This paper presented a novel IDS framework that integrated Explainable Artificial Intelligence (XAI) to enhance transparency in deep learning models. The framework was evaluated experimentally using the benchmark dataset NSL-KDD, demonstrating superior performance compared to traditional IDS and black
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