Enhancing cybersecurity with Explainable Artificial Intelligence: technical framework and applications in training labs

Abstract

Cyberattacks are growing in complexity, and machine-learning-based intrusion detection systems (IDS) are increasingly adopted to support scalable threat monitoring. However, high-performing models can be operationally difficult to deploy when their decisions are not interpretable or auditable. This paper studies explainability as a decision-support component in an IDS workflow rather than as a purely visual add-on. Using the UNSW-NB15 benchmark, we compare three widely used classifiers—Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM)—and then analyse the strongest performer (RF) with post-hoc explainability tools: Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). RF achieved 95.3% accuracy (precision 94.8%, recall 96.1%, F1-score 95.4%), exceeding DT and SVM on the same split. LIME and SHAP consistently highlighted traffic-volume and duration-related features (e.g., destination bytes, source bytes, and flow duration) as influential drivers of intrusion predictions, providing actionable hypotheses for analyst triage and policy refinement. We further discuss how explanation outputs can be operationalized in cybersecurity training labs through auditable “rationale artifacts,” while clarifying that any observed reduction in false positives should be interpreted as the outcome of explanation-guided interventions (e.g., threshold tuning and triage rule adjustments) rather than a direct causal effect of generating explanations. Finally, we outline necessary research extensions—controlled baselines, robustness testing, and explanation stability/faithfulness analysis—to ensure reliable deployment of LIME/SHAP in safety-critical IDS settings.