Network Intrusion Detection

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14 papers in the last four weeks, up 367% on the four weeks before. 0.1% of all new papers.

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Latest papers 109

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CLAD: A Clustered Label-Agnostic Federated Learning Framework for Joint Anomaly Detection and Attack Classification
May 7, 2026Iason Ofeidis, Nikos Papadis, Randeep Bhatia +2IoT Intrusion Detection Network Intrusion Detection
Yale University, New Haven, CT, USA · Nokia Bell Labs, Murray Hill, NJ, USA
parHSOM: A novel parallel Hierarchical Self-Organizing Map implementation
May 4, 2026Rebekah Lane, Logan Cummins, Andy Perkins +3Network Intrusion Detection Self-Organizing Maps
Computer Science & Engineering Mississippi State University Starkville, United States · Computer Science University of Alabama Tuscaloosa, United States
Evaluating Tabular Representation Learning for Network Intrusion Detection
May 4, 2026Muhammad Usman Butt, Andreas Hotho, Daniel SchlörRepresentation Learning Network Intrusion Detection
Julius-Maximilians-Universität Würzburg Würzburg, Germany · Data Science Chair Center for Artificial Intelligence and Data Science Julius-Maximilians-Universität Würzburg Würzburg, Germany
Robust and Explainable Divide-and-Conquer Learning for Intrusion Detection
May 3, 2026Yan Zhou, Kevin Hamlen, Michael De Lucia +5Explainable Artificial Intelligence Network Intrusion Detection
University of Texas at Dallas, Richardson, TX · DEVCOM, Army Research Laboratory, Adelphi, MD · Department of Computer Science, Virginia Tech, Blacksburg, VA +1
Decompose to Understand, Fuse to Detect: Frequency-Decoupled Anomaly Detection for Encrypted Network Traffic
May 3, 2026Xinglin Lian, Chengtai Cao, Ting Zhong +3Frequency-Domain Feature Learning Network Intrusion Detection
School of Information and Software Engineering, University of Electronic Science and Technology of China · Department of Computer Science, City University of Hong Kong · Department of Computer Science and Engineering, Hong Kong University of Science and Technology +1
A Comparative Analysis of Machine Learning Models for Intrusion Detection in Intelligent Transport Systems
Apr 30, 2026Zawad Yalmie Sazid, Robert Abbas, Sasa MaricIntelligent Transportation Systems Network Intrusion Detection
Victoria University, Sydney, Australia · University of New South Wales College, Sydney, Australia
Medoid Prototype Alignment for Cross-Plant Unknown Attack Detection in Industrial Control Systems
Apr 28, 2026Luyao WangDomain Adaptation Network Intrusion Detection
University of Malaya Malaysia
CAN-QA: A Question-Answering Benchmark for Reasoning over In-Vehicle CAN Traffic
Apr 27, 2026Jing Chen, Abhijay Deevi, Onat Gungor +1Temporal Reasoning Network Intrusion Detection
Department of Computer Science and Engineering University of California, San Diego (UCSD)
A-THENA: Early Intrusion Detection for IoT with Time-Aware Hybrid Encoding and Network-Specific Augmentation
Apr 23, 2026Ioannis Panopoulos, Maria Lamprini A. Bartsioka, Sokratis Nikolaidis +3IoT Intrusion Detection Network Intrusion Detection
National Technical University of Athens, Greece · Samsung AI Center, UK
DAIRE: A lightweight AI model for real-time detection of Controller Area Network attacks in the Internet of Vehicles
Apr 22, 2026Shahid Alam, Amina Jameel, Zahida Parveen +4Network Intrusion Detection Cybersecurity
Department of Information Security, College of Computer Science and Engineering, University of Ha’il, Ha’il, Saudi Arabia · Department of Computer Engineering, Center of Excellence in Artificial Intelligence, Bahria University, Islamabad, Pakistan · Department of Artificial Intelligence, College of Computer Science and Engineering, University of Ha’il, Ha’il, Saudi Arabia +2
SDNGuardStack: An Explainable Ensemble Learning Framework for High-Accuracy Intrusion Detection in Software-Defined Networks
Apr 22, 2026Ashikuzzaman, Md. Saifuzzaman Abhi, Mahabubur Rahman +3Ensemble Learning Network Intrusion Detection
Dept. of CSE University of Barishal Barishal, Bangladesh · Dept. of CSE University of Scholars Dhaka, Bangladesh
Enhancing Anomaly-Based Intrusion Detection Systems with Process Mining
Apr 20, 2026Francesco Vitale, Francesco Grimaldi, Massimiliano Rak +1Process Mining Network Intrusion Detection
DIETI University of Naples Federico II Naples, Italy
ExAI5G: A Logic-Based Explainable AI Framework for Intrusion Detection in 5G Networks
Apr 20, 2026Saeid Sheikhi, Panos Kostakos, Lauri LovenExplainable Artificial Intelligence Network Intrusion Detection
Faculty of Information Technology and Electrical Engineering, University of Oulu, , Oulu, 90570, , Finland
Evaluating Temporal and Structural Anomaly Detection Paradigms for DDoS Traffic
Apr 17, 2026Yasmin Souza Lima, Rodrigo Moreira, Larissa F. Rodrigues Moreira +2Network Intrusion Detection Unsupervised Anomaly Detection
Institute of Exact and Technological Sciences Federal University of Viçosa (UFV) – MG – Brazil · University of São Paulo (USP) 05.508-010 – São Paulo – SP – Brazil · Department of Informatics – School of Engineering University of Minho (UMinho) – Braga – Portugal
MLDAS: Machine Learning Dynamic Algorithm Selection for Software-Defined Networking Security
Apr 16, 2026Pablo Benlloch, Oscar Romero, Antonio Leon +1Algorithm Selection Network Intrusion Detection
Departamento de Comunicaciones, Universitat Politecnica de Valencia
Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigation
Sep 24, 2025Tharcisse Ndayipfukamiye, Jianguo Ding, Doreen Sebastian Sarwatt +2Network Intrusion Detection Adversarial Robustness
Department of Computer Science and Technology, University of Science and Technology Beijing · Department of Computer Science, Blekinge Institute of Technology
Risk-Calibrated Bayesian Streaming Intrusion Detection with SRE-Aligned Decisions
Sep 17, 2025Michel A. YoussefCost-Sensitive Learning Probability Calibration
Independent Researcher · Independent Researcher, Lebanon
FALCON: Transforming Cyber Threat Intelligence into Deployable IDS Rules with Self-Reflection
Aug 26, 2025Shaswata Mitra, Subash Neupane, Martin Duclos +5Cyber Threat Intelligence Network Intrusion Detection
The University of Alabama · Meharry Medical College · The University of Texas at El Paso
CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection
Apr 2, 2025Diego Cajaraville-Aboy, Marta Moure-Garrido, Carlos Beis-Penedo +5Network Intrusion Detection Encrypted Network Traffic Analysis
atlanTTic Research Center – ICLAB – Universidade de Vigo, Vigo, 36310, Spain · Department of Telematic Engineering, Universidad Carlos III de Madrid, Spain

May 7, 2026, cs.LGJ/K move · Enter open · S save

CLAD: A Clustered Label-Agnostic Federated Learning Framework for Joint Anomaly Detection and Attack Classification

Iason Ofeidis, Nikos Papadis, Randeep Bhatia, Leandros Tassiulas+1

Yale University, New Haven, CT, USA · Nokia Bell Labs, Murray Hill, NJ, USA

IoT Intrusion Detection Network Intrusion Detection Unsupervised Anomaly Detection Clustered Federated Learning Federated Learning

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The rapid expansion of the Internet of Things (IoT) and Industrial IoT (IIoT) has created a massive, heterogeneous attack surface that challenges traditional network security mechanisms. While Federated Learning (FL) offers a privacy-preserving alternative to centralized Intrusion Detection Systems (IDS), standard approaches struggle to generalize across diverse device behaviors and typically fail to utilize the vast amounts of unlabeled data present in realistic edge environments. To bridge these gaps, we propose CLAD, a holistic framework that seamlessly incorporates Clustered Federated Learning (CFL) with a novel Dual-Mode Micro-Architecture (

\text{DM}^2\text{A}

). This unified approach simultaneously tackles the two primary bottlenecks of IoT security: device heterogeneity and label scarcity. The

\text{DM}^2\text{A}

component features a shared encoder followed by two branches, enabling joint unsupervised anomaly detection and supervised attack classification; this allows the framework to harvest intelligence from both labeled and unlabeled clients. Concurrently, the clustering component dynamically groups devices with congruent traffic patterns, preventing global model divergence. By carefully combining these elements, CLAD ensures that no data is discarded and distinct operational patterns are preserved. Extensive evaluations demonstrate that this integrated approach significantly outperforms state-of-the-art baselines, achieving a 30% relative improvement in detection performance in scenarios with 80% unlabeled clients, with only half the communication cost.

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