arXiv Machine Learning June 10, 2026 By Abu Fuad Ahmad, Istiaque Ahmed

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

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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.

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benchmarks

arXiv Machine Learning

Jul 16

BARS: Benign-Anchored Ranking and Selection for False Alarm Reduction in Network Intrusion Detection

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

4d ago

Multi-Class, Multi-Tier Network Intrusion Detection: A Comprehensive and Reproducible Benchmark

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

benchmarks

arXiv Machine Learning

Aug 4

Explainable Hybrid Feature Selection for Intrusion Detection in Internet of Medical Things Environments

arXiv:2608. 00869v1 Announce Type: cross Abstract: Internet of Medical Things (IoMT) networks are hard to protect: devices are heterogeneous, computing resources are scarce, and traffic must be analyzed in real time.

By Amira Berrezzek, Hayet Djellali, Giulio Mallardi, Lamia Mahnane

arXiv AI

Jul 2

Cross-Domain Generalization Failure in Lightweight Intrusion Detection Models for IIoT Networks

arXiv:2607. 00553v1 Announce Type: cross Abstract: Lightweight machine learning models are increasingly proposed for intrusion detection in Industrial Internet of Things (IIoT) networks due to their suitability for resource-constrained edge deployment.

By MD Azizul Hakim, Md Shihab Uddin, Talha Ibne Anis

efficiency safety

arXiv Machine Learning

Jul 20

Improving Network Anomaly Detection via Choquet-Integral-Based Feature Aggregation

arXiv:2607. 15389v1 Announce Type: cross Abstract: This work investigates a generalized Choquet-integral-based feature aggregation framework to improve anomaly detection in high-dimensional network traffic data.

By Abreu Quevedo, Roger Immich, Giancarlo Lucca, Gra\c{c}aliz Dimuro, Bruno L. Dalmazo

arXiv AI

Jun 9

SHIELD-IDS: Structurally Heterogeneous Ensemble with Integrated Layered Defense for Intrusion Detection Systems

arXiv:2606. 07716v1 Announce Type: cross Abstract: Adversarial attacks pose a serious and growing threat to Machine Learning (ML)-based Intrusion Detection Systems (IDS), where imperceptible perturbations to network flow features can systematically mislead classifiers into accepting malicious traffic as benign.

By Maryam Zaman, Muhammad Khuram Shahzad

reinforcement-learning safety

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