RT-IoT2022
Catalog Summary
"Real-time network traffic dataset from diverse IoT devices including normal behavior and various attacks (DDoS, brute-force, scans) for developing intrusion detection systems."
Catalog Notes
Overview
The RT-IoT2022 dataset captures bidirectional network traffic from real IoT devices (e.g., ThingSpeak-LED, Wipro-Bulb, MQTT-Temp) under normal and adversarial conditions using Zeek and Flowmeter tools.
Data Collection
- Includes normal IoT traffic and simulated attacks such as Brute-Force SSH, DDoS (Hping, Slowloris), and Nmap scans.
- 123,117 instances with 83 engineered features from flow statistics.
Variables
- Flow attributes: durations, packet counts, inter-arrival times (min, max, mean, std).
- Protocol information, service types, packet sizes.
- Target label: Attack_type (multi-class including normal).
Typical Use Cases
- Multi-class classification and anomaly detection for IoT intrusion detection systems (IDS).
- Real-time network monitoring and cybersecurity in IoT environments.
- Benchmarking ML models on realistic IoT attack data.
License
Creative Commons Attribution 4.0 International (CC BY 4.0).
View Data Structure
To explore column names, data types, and sample rows, visit the official dataset page on UCI Machine Learning Repository.
Preview on UCI Machine Learning RepositoryCite This Dataset
B. S., & R. Nagapadma (2023). RT-IoT2022 [Data set]. UCI Machine Learning Repository. https://archive.ics.uci.edu/static/public/942/rt-iot2022.zip
Source metadata: UCI Machine Learning Repository (2023)
Indexed by IoTDataset.com on Jan 26, 2026
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