Network Intrusion Detection

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Overview

Network intrusion detection studies how to identify attacks and suspicious behavior from network flows, packet sequences, and related telemetry, often without inspecting encrypted payloads. Common approaches include supervised flow classifiers such as random forests and gradient boosting, anomaly detectors such as autoencoders, and temporal or graph models; federated learning lets organizations collaborate without sharing raw traffic. Researchers also explore detecting unseen attacks, explaining alerts, and resisting adversarial evasion, while adapting models to settings such as IoT, industrial control, and vehicle networks. A central challenge is that high benchmark scores may not transfer across networks or time, and reliable deployment must balance rare-attack detection against false alarms, latency, privacy, and resource limits.

Latest papers

CardsList

168 papers22 papers in the last four weeks, up 38% on the four weeks before. 0.1% of all new papers.

  1. In-network Attack Detection with Federated Deep Learning in IoT Networks: Real Implementation and Analysis

    Mar 23, 2026Devashish Chaudhary, Sutharshan Rajasegarar, Shiva Raj Pokhrel +2IoT Intrusion DetectionNetwork Intrusion Detection

    School of IT, Deakin University, Geelong, Australia · School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India

  2. Q-AGNN: Quantum-Enhanced Attentive Graph Neural Network for Intrusion Detection

    Mar 23, 2026Devashish Chaudhary, Sutharshan Rajasegarar, Shiva Raj PokhrelNetwork Intrusion DetectionGraph Attention Networks

    School of Information Technology, Deakin University, Geelong, Victoria, 3220, Australia.

  3. A Novel Solution for Zero-Day Attack Detection in IDS using Self-Attention and Jensen-Shannon Divergence in WGAN-GP

    Mar 19, 2026Ziyu Mu, Xiyu Shi, Safak DoganNetwork Intrusion DetectionGenerative Adversarial Networks

    Institute for Digital Technologies, Loughborough University, London, UK

  4. Machine Learning for Network Attacks Classification and Statistical Evaluation of Adversarial Learning Methodologies for Synthetic Data Generation

    Mar 18, 2026Iakovos-Christos Zarkadis, Christos DouligerisNetwork Intrusion DetectionSynthetic Data Generation

    University of Piraeus, Athens, Greece · Dept. of Informatics, University of Piraeus, Piraeus, Greece

  5. Deep learning based intelligent IDS for Large-scale IoT networks

    Mar 17, 2026Isha Andrade, Shalaka S Mahadik, Mithun Mukherjee +2IoT Intrusion DetectionNetwork Intrusion Detection

    Department of Computer Science, Birla Institute of Technology and Science, Pilani, Dubai Campus, Dubai International Academic City, Dubai, United Arab Emirates

  6. DNS-GT: A Graph-based Transformer Approach to Learn Embeddings of Domain Names from DNS Queries

    Mar 11, 2026Massimiliano Altieri, Ronan Hamon, Roberto Corizzo +2Network Intrusion Detection

    Joint Research Centre, European Commission, Ispra, 21027, Italy. · Department of Computer Science, American University, 4400 Massachusetts Avenue NW, Washington, 20016, DC, USA. · Department of Computer Science, University of Bari Aldo Moro, Via Orabona, 4, Bari, 70125, Italy.

  7. Enhancing Network Intrusion Detection Systems: A Multi-Layer Ensemble Approach to Mitigate Adversarial Attacks

    Mar 11, 2026Nasim Soltani, Shayan Nejadshamsi, Zakaria Abou El Houda +4Network Intrusion DetectionAdversarial Robustness

    Institut national de la recherche scientifique (INRS-EMT), Université du Québec, Montréal, Québec, Canada · INRS-UQO Mixed Research Unit on Cybersecurity, Gatineau, Québec, Canada · Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montréal, Québec, Canada +2

  8. NetDiffuser: Deceiving DNN-Based Network Attack Detection Systems with Diffusion-Generated Adversarial Traffic

    Mar 9, 2026Pratyay Kumar, Abu Saleh Md Tayeen, Satyajayant Misra +4Adversarial AttacksAdversarial Examples

    Department of Computer Science, New Mexico State University, Las Cruces, NM, USA · University of Hartford, CT, USA · DEVCOM Analysis Center, WSMR, NM, USA

  9. AMDS: Attack-Aware Multi-Stage Defense System for Network Intrusion Detection with Two-Stage Adaptive Weight Learning

    Mar 1, 2026Oluseyi Olukola, Nick RahimiNetwork Intrusion DetectionAdversarial Robustness

    School of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS, USA

  10. MI2^2DAS: A Multi-Layer Intrusion Detection Framework with Incremental Learning for Securing Industrial IoT Networks

    Feb 27, 2026Wei Lian, Alejandro Guerra-ManzanaresIoT Intrusion DetectionNetwork Intrusion Detection

    School of Computer Science, University of Nottingham, Ningbo, China

  11. ThreatFormer-IDS: Robust Transformer Intrusion Detection with Zero-Day Generalization and Explainable Attribution

    Feb 26, 2026Srikumar NayakExplainable Intrusion DetectionIoT Intrusion Detection

    Incedo Inc, USA

  12. Unsupervised Anomaly Detection in NSL-KDD Using ββ-VAE: A Latent Space and Reconstruction Error Approach

    Feb 23, 2026Dylan Baptiste, Ramla Saddem, Alexandre Philippot +1Unsupervised Anomaly DetectionNetwork Intrusion Detection

    CRESTIC · Université de Reims Champagne-Ardenne, CRESTIC, Reims, France · Seckiot, Paris, France

  13. Detecting Cybersecurity Threats by Integrating Explainable AI with SHAP Interpretability and Strategic Data Sampling

    Feb 22, 2026Norrakith Srisumrith, Sunantha SodseeStratified SamplingExplainable Artificial Intelligence

    Dept. of Digital Network and Information Security Management King Mongkut’s University of Technology North Bangkok Bangkok, Thailand

  14. Collaborative Zone-Adaptive Zero-Day Intrusion Detection for IoBT

    Feb 18, 2026Amirmohammad Pasdar, Shabnam Kasra Kermanshahi, Nour Moustafa +1Network Intrusion DetectionInternet of Things

    School of Computing and Information Systems, The University of Melbourne, VIC 3010, Australia · School of Systems & Computing, University of New South Wales, ACT 2612, Australia

  15. 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 DetectionAdversarial Robustness

    Department of Computer Science and Technology, University of Science and Technology Beijing · Department of Computer Science, Blekinge Institute of Technology

  16. Risk-Calibrated Bayesian Streaming Intrusion Detection with SRE-Aligned Decisions

    Sep 17, 2025Michel A. YoussefCost-Sensitive LearningProbability Calibration

    Independent Researcher · Independent Researcher, Lebanon

  17. FALCON: Transforming Cyber Threat Intelligence into Deployable IDS Rules with Self-Reflection

    Aug 26, 2025Shaswata Mitra, Subash Neupane, Martin Duclos +5Cyber Threat IntelligenceNetwork Intrusion Detection

    The University of Alabama · Meharry Medical College · The University of Texas at El Paso

  18. 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 DetectionEncrypted Network Traffic Analysis

    atlanTTic Research Center – ICLAB – Universidade de Vigo, Vigo, 36310, Spain · Department of Telematic Engineering, Universidad Carlos III de Madrid, Spain