Multi-Level Distributional Entropy from Flow Summary Statistics for Explainable Network Intrusion Detection
Machine learning network intrusion detection systems (IDS) operate on aggregate flow statistics that discard the distributional structure of traffic, and although information-theoretic measures capture that structure, established entropy estimators require raw packet sequences that pre-aggregated flow datasets do not contain. No prior method derives entropy from the summary statistics those records already hold. We introduce Multi-Level Distributional Entropy (MDE), which computes interpretable information-theoretic features analytically from flow-level summary statistics at three levels, within-flow Gaussian differential entropy, cross-directional Jensen-Shannon divergence (JSD), and Tra...
| Authors | Mohamed Aly Bouke, Md Shohel Sayeed, Swee-Huay Heng et al. |
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| Primary category | cs.CR |
| Published | 29 June 2026 |
| Last updated at source | 3 October 2026 |
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