RT-IoT2022: Real-Time IoT Intrusion Detection Dataset
Abstract
"RT-IoT2022 is a network traffic dataset specifically derived from a real-time IoT testbed containing smart home devices. It includes both normal traffic and various common network attacks."
Description
Overview
RT-IoT2022 captures representative network traffic from IoT devices (cameras, bulbs, etc.) to support the development of real-time IDS.
Technical Details
Includes attributes like duration, service type, flag, and byte counts. It covers attacks like ARP spoofing, MQTT attacks, and SYN flooding.
Collection Setup
Captured within a smart home testbed consisting of various IoT devices interacting over a local network.
Recommended Research Tasks
Real-time attack classification, lightweight IDS development, and feature importance analysis for IoT networks.
Access & License
Hosted at the UCI Machine Learning Repository. Access Dataset
📊 View Data Structure
To explore column names, data types, and sample rows, visit the official dataset page on Kaggle.
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Cite This Dataset
S., B., & Nagapadma, R. (2023). RT-IoT2022: Real-Time IoT Intrusion Detection Dataset. [Dataset]. {UCI Machine Learning Repository. https://doi.org/10.24432/C5P338
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Original source: {UCI Machine Learning Repository (2023). Visit official page for more details.
Indexed by IoTDataset.com on Feb 05, 2026
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