Benchmarking study demonstrates high-accuracy anomaly detection across network datasets, indicating enhanced transparency and tracking of feature shifts.
The exponential growth of network data and sophisticated cyber threats requires Intrusion Detection Systems (IDS) to possess both high accuracy and clear interpretability. This research proposes a comprehensive IDS framework that combines a hybrid Deep Learning architecture (CNN-LSTM) with the Synthetic Minority Over-sampling Technique (SMOTE) to efficiently identify anomalous behaviors. The focal point of this paper is the integration of the SHapley Additive exPlanations (SHAP) tool and adversarial evaluation approaches to break the "black-box" barrier of Deep Learning, providing transparent interpretations for every system decision. The system is empirically evaluated on modern benchmark datasets, demonstrating high performance in maintaining accuracy, enhancing human trust management, and tracking multi-context feature shifts.
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An et al. (2026) studied this question.
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