• Computer Science > Artificial Intelligence [Submitted on 4 Feb 2026] Title:Human-Centered Explainable AI for Security Enhancement: A Deep Intrusion Detection Framework View PDF HTML (experimental)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-box deep learning models. • The proposed approach combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks for capturing temporal dependencies in traffic sequences. • Our deep learning results showed that both CNN and LSTM reached 0.99 for accuracy, whereas LSTM outperformed CNN at macro average precision, recall, and F-1 score. • For weighted average precision, recall, and F-1 score, both models scored almost similarly.
Article Summaries:
- Computer Science > Artificial Intelligence [Submitted on 4 Feb 2026] Title:Human-Centered Explainable AI for Security Enhancement: A Deep Intrusion Detection Framework View PDF HTML (experimental)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 su
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