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.
arXiv:2609.05701v1 Announce Type: cross
Abstract: One of the biggest risks faced by Software Defined Networks (SDN) is the Distributed Denial of Service (DDoS) attack in which a compromised controlle...
By Adeel Ahmad, Ali Akarma, Ahmad Ali, Hammad Muneer, Toqeer Ali Syed
arXiv:2607. 15379v1 Announce Type: cross Abstract: Network anomaly detection is increasingly challenging due to the growing diversity and variability of traffic patterns, which are not always well captured by traditional statistical features.
By Iuri Mundstock, Abreu Quevedo, J\'eferson Campos Nobre, Roben C. Lunardi, Thiago L. T. da Silveira, Bruno L. Dalmazo
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
arXiv:2609.36039v1 Announce Type: cross
Abstract: Machine learning (ML) and deep learning (DL) have dominated Intrusion Detection System (IDS) research in recent years. Unfortunately, many existing s...
By Yufeng Xin, Bryant Goseland, Mohamed Rahouti
JEV-IDS is an open experimental general network intrusion detection system that uses the Jev System One Model to detect zero‑day intrusions even when labeled data are scarce. The system processes one flow per request and asks the model two questions: a binary attack probability and a finite‑choice traffic category. In tests on a 300‑flow NSL‑KDD pilot split, JEV-IDS achieved an F1‑score of 0.859, precision of 0.941, recall of 0.790, and a novel‑attack recall of 0.838, while being 4.8 times faster and 3.8 times cheaper than GPT‑5.6 Luna and producing 15 times fewer false alarms than a low‑data Random Forest.
By Paulo Severo, Silvio E. Quincozes, Amanda Dias
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
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
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
arXiv:2606. 11098v1 Announce Type: cross Abstract: Recent deep learning approaches for network intrusion detection increasingly incorporate temporal architectures such as recurrent networks and Transformers, often reporting near-perfect performance on CIC-IDS2017.
By Zach Moczkodan (Royal Military College of Canada, Kingston, Canada), Hany Ragab (Royal Military College of Canada, Kingston, Canada)
The paper investigates whether machine learning models for IoT intrusion detection truly learn attack patterns or rely on dataset shortcuts. Using the CyberFlowIoT-GICAP benchmark, the authors evaluate four learning paradigms across different feature sets and split strategies, finding that performance is largely driven by feature representation and that tree-based models can exploit temporal artifacts. The study also highlights asymmetric attack detectability and proposes a four-point protocol checklist for realistic evaluation.
By Uday Shankar Roy, Mahbuba Jahan Minu
arXiv:2607. 00763v1 Announce Type: cross Abstract: Digital forensic investigations of network intrusions require analytical outputs that are traceable, reproducible, and court-defensible - requirements existing machine learning pipelines do not satisfy, since they treat original evidence as training data and produce opaque classifications without instance-level justification.
By Jose Luis Vela Alonso, Carmen Pellicer