Smart Home Intrusion Detection Dataset — 7 Attack Scenarios
Smart home traffic captured under normal operation and seven multi-stage attack scenarios across heterogeneous end devices. CC BY 4.0, published 2026.
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Smart home traffic captured under normal operation and seven multi-stage attack scenarios across heterogeneous end devices. CC BY 4.0, published 2026.
View DatasetFree CC0 synthetic dataset: 400 rows of indoor temperature, humidity, CO2, light and occupancy readings. 22°C. Reproducible from its seed.
View DatasetFree CC0 synthetic dataset: 1,000 rows of indoor temperature, humidity, CO2, light and occupancy readings. Cold Climate, 20.5°C.
View DatasetFree CC0 synthetic dataset: 10,000 rows of indoor temperature, humidity, CO2, light and occupancy readings. Tropical Climate, 22°C.
View DatasetFree CC0 synthetic dataset: 10,000 rows of indoor temperature, humidity, CO2, light and occupancy readings. Cold Climate, 22°C.
View DatasetFree CC0 synthetic dataset: 2,000 rows of indoor temperature, humidity, CO2, light and occupancy readings. Arid Climate, 21°C. Reproducible from its seed.
View DatasetFree CC0 synthetic dataset: 100 rows of indoor temperature, humidity, CO2, light and occupancy readings. 26.5°C. Reproducible from its seed.
View DatasetFree CC0 synthetic dataset: 1,000 rows of indoor temperature, humidity, CO2, light and occupancy readings. Reproducible from its seed.
View DatasetComprehensive smart home dataset with 1,048,575 rows and 31 columns including timestamps, device states (TV, oven, lights, fridge) and activity labels for machine learning classification of daily activities.
View DatasetBCCC-IoT-IDS-Zwave-2025 is a behavior-centric cybersecurity dataset focusing on Z-wave protocol vulnerabilities and intrusion detection for modern smart home automation systems.
View DatasetA large-scale, reproducible network dataset for evaluating modern IoT intrusion detection systems.
View DatasetComprehensive smart home dataset with 1,048,575 rows and 31 columns including timestamp, device states (TV, oven, lights, fridge) and activity labels for machine learning applications.
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