arXiv / Security Conference
Gotham Dataset 2025: Reproducible IoT Security Research
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Catalog Summary
"A large-scale, reproducible network dataset for evaluating modern IoT intrusion detection systems."
Catalog Notes
The Gotham Dataset (2025) was created to solve the 'reproducibility crisis' in IoT security research. It provides a standardized environment for testing Intrusion Detection Systems (IDS) against modern threats. The dataset includes a wide array of network traffic captures from smart home environments, simulating various device interactions and attack scenarios. It focuses on high-fidelity labeling and provides detailed metadata for every packet, including protocol flags and inter-arrival times. Gotham is particularly useful for evaluating the generalizability of machine learning models across different IoT network configurations, offering a robust platform for both academic and industrial security benchmarking.
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Preview on arXiv / Security ConferenceCite This Dataset
Barker, J., & others (2025). Gotham Dataset 2025: A Reproducible Large-Scale IoT Network Dataset. [Dataset]. arXiv / Security Conference. https://arxiv.org/abs/2502.03134
Source metadata: arXiv / Security Conference (2025)
Indexed by IoTDataset.com on Feb 12, 2026
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