Gotham Dataset 2025: A Reproducible Large-Scale IoT Network Dataset for Intrusion Detection and Security Research
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912221239443456 |
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| author | Belarbi, Othmane Spyridopoulos, Theodoros Anthi, Eirini Rana, Omer Carnelli, Pietro Khan, Aftab |
| author_facet | Belarbi, Othmane Spyridopoulos, Theodoros Anthi, Eirini Rana, Omer Carnelli, Pietro Khan, Aftab |
| contents | In this paper, a dataset of IoT network traffic is presented. Our dataset was generated by utilising the Gotham testbed, an emulated large-scale Internet of Things (IoT) network designed to provide a realistic and heterogeneous environment for network security research. The testbed includes 78 emulated IoT devices operating on various protocols, including MQTT, CoAP, and RTSP. Network traffic was captured in Packet Capture (PCAP) format using tcpdump, and both benign and malicious traffic were recorded. Malicious traffic was generated through scripted attacks, covering a variety of attack types, such as Denial of Service (DoS), Telnet Brute Force, Network Scanning, CoAP Amplification, and various stages of Command and Control (C&C) communication. The data were subsequently processed in Python for feature extraction using the Tshark tool, and the resulting data was converted to Comma Separated Values (CSV) format and labelled. The data repository includes the raw network traffic in PCAP format and the processed labelled data in CSV format. Our dataset was collected in a distributed manner, where network traffic was captured separately for each IoT device at the interface between the IoT gateway and the device. Our dataset was collected in a distributed manner, where network traffic was separately captured for each IoT device at the interface between the IoT gateway and the device. With its diverse traffic patterns and attack scenarios, this dataset provides a valuable resource for developing Intrusion Detection Systems and security mechanisms tailored to complex, large-scale IoT environments. The dataset is publicly available at Zenodo. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_03134 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Gotham Dataset 2025: A Reproducible Large-Scale IoT Network Dataset for Intrusion Detection and Security Research Belarbi, Othmane Spyridopoulos, Theodoros Anthi, Eirini Rana, Omer Carnelli, Pietro Khan, Aftab Cryptography and Security Artificial Intelligence In this paper, a dataset of IoT network traffic is presented. Our dataset was generated by utilising the Gotham testbed, an emulated large-scale Internet of Things (IoT) network designed to provide a realistic and heterogeneous environment for network security research. The testbed includes 78 emulated IoT devices operating on various protocols, including MQTT, CoAP, and RTSP. Network traffic was captured in Packet Capture (PCAP) format using tcpdump, and both benign and malicious traffic were recorded. Malicious traffic was generated through scripted attacks, covering a variety of attack types, such as Denial of Service (DoS), Telnet Brute Force, Network Scanning, CoAP Amplification, and various stages of Command and Control (C&C) communication. The data were subsequently processed in Python for feature extraction using the Tshark tool, and the resulting data was converted to Comma Separated Values (CSV) format and labelled. The data repository includes the raw network traffic in PCAP format and the processed labelled data in CSV format. Our dataset was collected in a distributed manner, where network traffic was captured separately for each IoT device at the interface between the IoT gateway and the device. Our dataset was collected in a distributed manner, where network traffic was separately captured for each IoT device at the interface between the IoT gateway and the device. With its diverse traffic patterns and attack scenarios, this dataset provides a valuable resource for developing Intrusion Detection Systems and security mechanisms tailored to complex, large-scale IoT environments. The dataset is publicly available at Zenodo. |
| title | Gotham Dataset 2025: A Reproducible Large-Scale IoT Network Dataset for Intrusion Detection and Security Research |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2502.03134 |