Hugging Face Trending Papers June 29, 2026

Multi-Level Distributional Entropy for Explainable Network Intrusion Detection

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Machine learning network intrusion detection systems (IDS) rely on aggregate flow statistics that discard distributional structure, while established entropy measures require raw packet sequences unavailable in pre-aggregated flow datasets. We propose Multi-Level Distributional Entropy (MDE), an analytical framework that derives interpretable entropy features directly from flow-level summary statistics at three levels: within-flow Gaussian differential entropy, cross-directional Jensen-Shannon divergence (JSD), and Transmission Control Protocol (TCP) flag-pattern Shannon entropy, without raw packet access or training data.

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arXiv AI

Jun 30

Multi-Level Distributional Entropy for Explainable Network Intrusion Detection

arXiv:2606. 29797v1 Announce Type: cross Abstract: Machine learning network intrusion detection systems (IDS) rely on aggregate flow statistics that discard distributional structure, while established entropy measures require raw packet sequences unavailable in pre-aggregated flow datasets.

By Mohamed Aly Bouke, Md Shohel Sayeed, Swee-Huay Heng, Azizol Abdullah, Mohamed Othman

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arXiv Machine Learning

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Jul 16

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arXiv:2607. 13203v1 Announce Type: cross Abstract: False alarms remain a major barrier to deploying network intrusion detection systems (NIDS).

By Abu Fuad Ahmad, Istiaque Ahmed

efficiency benchmarks safety

arXiv Machine Learning

Jun 10

nCMD: Benign-Anchored Feature Selection for Imbalanced Network Intrusion Detection

arXiv:2606. 09934v1 Announce Type: new Abstract: Feature selection is critical for network intrusion detection systems (NIDS) operating under high-dimensional, highly imbalanced traffic, as found in operational and defense networks.

By Abu Fuad Ahmad, Istiaque Ahmed

benchmarks

arXiv AI

Jun 2

A Protocol-Language Model for Network Intrusion (Without Deep Packet Inspection)

arXiv:2606. 00155v1 Announce Type: cross Abstract: Modern network intrusion detection systems (NIDS) are caught in a structural contradiction: the protocols carrying the highest threat intelligence are precisely those encrypted under TLS 1.

By Vivek Kumar Sharma

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arXiv Machine Learning

1d ago

Provenance, Not Behaviour: A Serialisation Artifact in Edge-IIoTset and a Leakage-Free Benchmark for Precision-Agriculture Intrusion Detection

arXiv:2608. 15761v1 Announce Type: cross Abstract: Edge-IIoTset is the reference benchmark for machine-learning intrusion detection in the industrial Internet of Things, and results reported on it cluster above 99%.

By Mostafa M. Galal

benchmarks

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Multi-Level Distributional Entropy for Explainable Network Intrusion Detection

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