mlsanityData to 7 Oct 2026

Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset

arXiv 1811.00701 ↗First posted 2 Nov 2018cs.CRcited by 98

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Abstract (arXiv)

The proliferation of IoT systems, has seen them targeted by malicious third parties. To address this, realistic protection and investigation countermeasures need to be developed. Such countermeasures include network intrusion detection and network forensic systems. For that purpose, a well-structured and representative dataset is paramount for training and validating the credibility of the systems. Although there are several network, in most cases, not much information is given about the Botnet scenarios that were used. This paper, proposes a new dataset, Bot-IoT, which incorporates legitimate and simulated IoT network traffic, along with various types of attacks. We also present a realistic testbed environment for addressing the existing dataset drawbacks of capturing complete network information, accurate labeling, as well as recent and complex attack diversity. Finally, we evaluate the reliability of the BoT-IoT dataset using different statistical and machine learning methods for forensics purposes compared with the existing datasets. This work provides the baseline for allowing botnet identificaiton across IoT-specifc networks. The Bot-IoT dataset can be accessed at [1].

Key facts. Cited by 98 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Nov 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 14; 2023: 21; 2024: 19; 2025: 19; 2026: 14.

98 papers.

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  1. 2021E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTcited by 55
  2. 2021Federated Learning for Malware Detection in IoT Devicescited by 36
  3. 2022Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networkscited by 25
  4. 2024A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directionscited by 25
  5. 2021Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challengescited by 16
  6. 2022IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspectivecited by 15
  7. 2023Unsupervised Deep Learning for IoT Time Seriescited by 11
  8. 2023Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classificationcited by 10
  9. 2021Launching Adversarial Attacks against Network Intrusion Detection Systems for IoTcited by 9
  10. 2021An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networkscited by 8
  11. 2021Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detectioncited by 8
  12. 2021Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Reviewcited by 8
38 more
  1. 2022On Generalisability of Machine Learning-based Network Intrusion Detection Systemscited by 8
  2. 2022Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threatscited by 7
  3. 2024Use of Graph Neural Networks in Aiding Defensive Cyber Operationscited by 7
  4. 2024Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural Networkcited by 7
  5. 2021IoT Security: Botnet detection in IoT using Machine learningcited by 6
  6. 2022Machine Learning for Encrypted Malicious Traffic Detection: Approaches, Datasets and Comparative Studycited by 6
  7. 2025Impact of Latent Space Dimension on IoT Botnet Detection Performance: VAE-Encoder Versus ViT-Encodercited by 6
  8. 2021Exploring Edge TPU for Network Intrusion Detection in IoTcited by 5
  9. 2022HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detectioncited by 5
  10. 2024Brain-on-Switch: Towards Advanced Intelligent Network Data Plane via NN-Driven Traffic Analysis at Line-Speedcited by 5
  11. 2025IoT Botnet Detection: Application of Vision Transformer to Classification of Network Flow Trafficcited by 5
  12. 2025Enhancing IoT-Botnet Detection using Variational Auto-encoder and Cost-Sensitive Learning: A Deep Learning Approach for Imbalanced Datasetscited by 5
  13. 2021A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detectioncited by 4
  14. 2022Joint Semantic Transfer Network for IoT Intrusion Detectioncited by 4
  15. 2023Trustworthy Federated Learning: A Surveycited by 4
  16. 2024Survey of Graph Neural Network for Internet of Things and NextG Networkscited by 4
  17. 2025Cyber-Physical Systems Security: A Comprehensive Review of Anomaly Detection Techniquescited by 4
  18. 2022Edge Security: Challenges and Issuescited by 3
  19. 2022NFDLM: A Lightweight Network Flow based Deep Learning Model for DDoS Attack Detection in IoT Domainscited by 3
  20. 2022Network Intrusion Detection System in a Light Bulbcited by 3
  21. 2023Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approachcited by 3
  22. 2023Network Traffic Classification based on Single Flow Time Series Analysiscited by 3
  23. 2023NetTiSA: Extended IP Flow with Time-series Features for Universal Bandwidth-constrained High-speed Network Traffic Classificationcited by 3
  24. 2025Evaluating Language Models For Threat Detection in IoT Security Logscited by 3
  25. 2022LBDMIDS: LSTM Based Deep Learning Model for Intrusion Detection Systems for IoT Networkscited by 2
  26. 2022Investigation and rectification of NIDS datasets and standardized feature set derivation for network attack detection with graph neural networkscited by 2
  27. 2023Federated Deep Learning for Intrusion Detection in IoT Networkscited by 2
  28. 2023Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detectioncited by 2
  29. 2023Enhancing IoT Security via Automatic Network Traffic Analysis: The Transition from Machine Learning to Deep Learningcited by 2
  30. 2024EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learningcited by 2
  31. 2024Individual Packet Features are a Risk to Model Generalisation in ML-Based Intrusion Detectioncited by 2
  32. 2024Efficient Network Traffic Feature Sets for IoT Intrusion Detectioncited by 2
  33. 2024PPT-GNN: A Practical Pre-Trained Spatio-Temporal Graph Neural Network for Network Securitycited by 2
  34. 2024Towards a graph-based foundation model for network traffic analysiscited by 2
  35. 2025Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systemscited by 2
  36. 2025Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Surveycited by 2
  37. 2026Q-AGNN: Quantum-Enhanced Attentive Graph Neural Network for Intrusion Detectioncited by 2
  38. 2021Robust Attack Detection Approach for IIoT Using Ensemble Classifiercited by 1

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