Abstract

Identifying malicious traffic is crucial for safeguarding internal networks from privacy breaches. Intrusion Detection Systems (IDS) traditionally rely on inefficient and outdated rule-sets, necessitating a shift towards AI-driven, learning-based algorithms for enhanced detection capabilities. Despite their promise, AI-integrated IDS face deployment challenges due to complex, opaque decision-making processes that can lead to latency and an increased risk of false positives. This paper presents the Explainable AI-based Intrusion Detection System (XAI-IDS), addressing the limitations of both rule-based and AI-driven IDS by integrating interpretable deep learning models. XAI-IDS employs tree regularization to transform complex models into efficient, transparent decision trees, facilitating real-time detection with improved accuracy and explainability. Experiments on two benchmark datasets demonstrate XAI-IDS's superior performance, offering a scalable solution to the challenge of identifying malicious traffic with reduced risk of false positives.