Kaggle
IoT-DIAD 2024 - Device Identification and Anomaly Detection
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Catalog Summary
"Comprehensive IoT attack dataset for device identification and anomaly detection in security analytics applications."
Catalog Notes
A comprehensive IoT attack dataset designed for both IoT device identification and anomaly detection, aiming to advance security analytics applications. Developed by the Canadian Institute for Cybersecurity (CIC), this dataset provides network traffic captures from over 100 different IoT device types under normal and attack conditions. Features include deep packet inspection attributes, protocol-specific features, timing characteristics, and device fingerprinting parameters. Attack categories include malware communication, data exfiltration, command injection, and lateral movement. Supports research on zero-trust IoT security, device behavioral analysis, and automated threat hunting.
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Rabbani, Mahdi, Gui, Jinkun, Nejati, Fatemeh, Zhou, Zeming, Kaniyamattam, Arun, Mirani, Mansur, Piya, Gunjan, Opushnyev, Igor, Lu, Rongxing, & Ghorbani, Ali A. (2025). Device Identification and Anomaly Detection in IoT Environments. IEEE Internet of Things Journal. [Dataset]. Kaggle. https://doi.org/10.1109/JIOT.2024.3522863
Source metadata: Kaggle (2025) · DOI: 10.1109/JIOT.2024.3522863
Indexed by IoTDataset.com on Feb 19, 2026
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Related Topics & Keywords
#IoT
#Cybersecurity
#Device Identification
#Anomaly Detection
#CIC
#Security Analytics
#Threat Hunting
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