Beyond the Black Box: Toward an Explainable Machine Learning Framework for Zero-Day Attack Detection

Abstract

The escalating sophistication of network intrusions, particularly zero-day exploits that evade signature-based defenses,
has intensified interest in machine learning-driven intrusion detection systems capable of recognizing previously unseen
attack patterns. Yet the opacity of high-performing deep learning classifiers continues to undermine analyst trust, regulatory
compliance, and operational accountability. This paper undertakes a qualitative, synthesis-oriented investigation of the
intersection between explainable artificial intelligence and zero-day attack detection, asking how interpretability methods such
as SHAP, LIME, and attention mechanisms can be integrated systematically into detection pipelines without compromising
accuracy or computational feasibility. Employing a structured narrative literature review supplemented by a comparative
analytical framework, the study synthesizes findings from twenty-three peer-reviewed and preprint sources spanning intrusion
detection architectures, explainability techniques, and benchmark datasets. The analysis reveals that post-hoc, model-agnostic
explanation methods have become the dominant interpretability paradigm in zero-day contexts, that hybrid deep learning
architectures combining recurrent and attention-based components achieve superior detection fidelity, and that persistent
gaps remain in explanation stability, computational overhead, and human-centered evaluation of security explanations.
The paper proposes a conceptual pipeline architecture that positions explainability as an operational layer situated between
anomaly detection and analyst decision-making rather than a cosmetic addition appended after deployment. It concludes that
meaningful progress in zero-day defense depends on treating interpretability as a design constraint from inception, with direct
implications for security operations centers, regulatory auditing practice, and future empirical research on human-artificial
intelligence collaboration in cybersecurity contexts.