mlsanityData to 7 Oct 2026

A Survey of Network-based Intrusion Detection Data Sets

arXiv 1903.02460 ↗First posted 6 Mar 2019cs.CRcited by 46

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Abstract (arXiv)

Labeled data sets are necessary to train and evaluate anomaly-based network intrusion detection systems. This work provides a focused literature survey of data sets for network-based intrusion detection and describes the underlying packet- and flow-based network data in detail. The paper identifies 15 different properties to assess the suitability of individual data sets for specific evaluation scenarios. These properties cover a wide range of criteria and are grouped into five categories such as data volume or recording environment for offering a structured search. Based on these properties, a comprehensive overview of existing data sets is given. This overview also highlights the peculiarities of each data set. Furthermore, this work briefly touches upon other sources for network-based data such as traffic generators and traffic repositories. Finally, we discuss our observations and provide some recommendations for the use and creation of network-based data sets.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Mar 2019.

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Citing papers per year: 2021: 8; 2022: 8; 2023: 4; 2024: 3; 2025: 6; 2026: 17.

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  1. 2024TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Modelscited by 22
  2. 2022The Role of Machine Learning in Cybersecuritycited by 21
  3. 2023SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detectioncited by 13
  4. 2021MemStream: Memory-Based Streaming Anomaly Detectioncited by 12
  5. 2022AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detectioncited by 11
  6. 2021Sketch-Based Anomaly Detection in Streaming Graphscited by 9
  7. 2025Large Language Models for Security Operations Centers: A Comprehensive Surveycited by 9
  8. 2022The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systemscited by 8
  9. 2021Benchmarking the Benchmark -- Analysis of Synthetic NIDS Datasetscited by 5
  10. 2021Active Learning for Network Traffic Classification: A Technical Studycited by 5
  11. 2021An Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attackscited by 5
  12. 2024Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer Networkscited by 5
34 more
  1. 2026OrgForge-IT: A Verifiable Synthetic Benchmark for LLM-Based Insider Threat Detectioncited by 4
  2. 2021Bridging the gap to real-world for network intrusion detection systems with data-centric approachcited by 2
  3. 2022Investigation and rectification of NIDS datasets and standardized feature set derivation for network attack detection with graph neural networkscited by 2
  4. 2025Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systemscited by 2
  5. 2021Improving the Reliability of Network Intrusion Detection Systems through Dataset Integrationcited by 1
  6. 2022Prepare for Trouble and Make it Double. Supervised and Unsupervised Stacking for AnomalyBased Intrusion Detectioncited by 1
  7. 2023Leveraging a Probabilistic PCA Model to Understand the Multivariate Statistical Network Monitoring Framework for Network Security Anomaly Detectioncited by 1
  8. 2024Diffusion-based Adversarial Purification for Intrusion Detectioncited by 1
  9. 2025From Flows to Words: Can Zero-/Few-Shot LLMs Detect Network Intrusions? A Grammar-Constrained, Calibrated Evaluation on UNSW-NB15cited by 1
  10. 2025An Evaluation Framework for Network IDS/IPS Datasets: Leveraging MITRE ATT&CK and Industry Relevance Metricscited by 1
  11. 2026BRIDGE and TCH-Net: Heterogeneous Benchmark and Multi-Branch Baseline for Cross-Domain IoT Botnet Detectioncited by 1
  12. 2026Spectral Geometry and Bosonic-Bloch Probes: Explorations in Quantum Learningcited by 1
  13. 2021Data Curation and Quality Assurance for Machine Learning-based Cyber Intrusion Detectioncited by 0
  14. 2022DDoSDet: An approach to Detect DDoS attacks using Neural Networkscited by 0
  15. 2022Open ERP System Data For Occupational Fraud Detectioncited by 0
  16. 2022Improving Multilayer-Perceptron(MLP)-based Network Anomaly Detection with Birch Clustering on CICIDS-2017 Datasetcited by 0
  17. 2023Anomaly Detection Dataset for Industrial Control Systemscited by 0
  18. 2023Ensembling Uncertainty Measures to Improve Safety of Black-Box Classifierscited by 0
  19. 2025Packet-Level DDoS Data Augmentation Using Dual-Stream Temporal-Field Diffusioncited by 0
  20. 2025Revisiting Network Traffic Analysis: Compatible network flows for ML modelscited by 0
  21. 2026AMDS: Attack-Aware Multi-Stage Defense System for Network Intrusion Detection with Two-Stage Adaptive Weight Learningcited by 0
  22. 2026Explainable Threat Attribution for IoT Networks Using Conditional SHAP and Flow Behavior Modellingcited by 0
  23. 2026Robust Semi-Supervised Temporal Intrusion Detection for Adversarial Cloud Networkscited by 0
  24. 2026GESR: Graph-Based Edge Semantic Reconstruction for Stealthy Communication Detection with Benign-Only Trainingcited by 0
  25. 2026Context-Aware Web Attack Detection in Open-Source SIEM Systems via MITRE ATT&CK-Enriched Behavioral Profilingcited by 0
  26. 2026Stream Assembly Is an Uncontrolled Treatment in Streaming Intrusion-Detection Benchmarkscited by 0
  27. 2026An Improved CNN-LSTM Based Intrusion Detection System for IoT Networkscited by 0
  28. 2026nCMD: Benign-Anchored Feature Selection for Imbalanced Network Intrusion Detectioncited by 0
  29. 2026Do Transformers Actually Help Intrusion Detection? A Temporal Sequence Evaluation on CIC-IDS2017cited by 0
  30. 2026Multi-Level Distributional Entropy from Flow Summary Statistics for Explainable Network Intrusion Detectioncited by 0
  31. 2026Generative AI and Federated Learning for Intrusion Detection Systems: A Surveycited by 0
  32. 2026BARS: Benign-Anchored Ranking and Selection for False Alarm Reduction in Network Intrusion Detectioncited by 0
  33. 2026Defending Retrieval-Augmented Intrusion Detection Against Knowledge Poisoning and Prompt Injectioncited by 0
  34. 2026VQC-ZTI: Variational Quantum Control for Zero Trust Protection of the Tactile Internetcited by 0