Explainable Artificial Intelligence for Transparent Network Threat Detection Systems

In this research, an Explainable Artificial Intelligence (XAI)-based network threat detection system has been pro-posed to address the limitations of traditional and black-box AI models in cybersecurity. The study highlights the importance of combining high-performance machine learning [25], [26] techniques with interpretability to build trustworthy and efficient security systems. By integrating models such as Random Forest, Support Vector Machine (SVM), and Neural Networks with explainability tools like SHAP and LIME, the system is able to not only detect threats accurately but also provide clear justifications for its decisions. The experimental results demonstrate that the proposed approach achieves strong performance in terms of accuracy, precision, recall, and F1-score, while significantly improving transparency. The inclusion of the explainability layer [27], [28] allows cybersecurity analysts to understand the reasoning behind threat classifications, making it easier to validate re-sults, reduce false positives, and improve response strategies. This is especially important in real-world scenarios where decision accountability and trust are critical. Moreover, the multi-layered architecture ensures efficient data handling, from collection and preprocessing to detection and response. It supports real-time monitoring and automated mitigation, making it suitable for deployment in modern network environments.

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