Edge-IIoTset - Comprehensive Cyber Security Dataset for IoT and IIoT
Catalog Summary
"Realistic cybersecurity dataset with 14 attack types from 10+ IoT/IIoT device types including sensors, actuators, and industrial controllers. Supports centralized and federated learning with 61 optimized features."
Catalog Notes
Dataset Introduction
The Edge-IIoTset dataset represents a major breakthrough in IoT cybersecurity research, published in IEEE Access (2022) and cited over 1,012 times. Developed by researchers from Edith Cowan University, this dataset provides unparalleled realism for industrial and consumer IoT security research.
Device Ecosystem
This dataset captures traffic from an extensive IoT/IIoT testbed featuring environmental sensors (DHT11/DHT22, pressure, gas), industrial sensors (ultrasonic, water level, soil moisture, pH meters), medical IoT devices (heart rate monitors, pulse oximeters), safety systems (flame, smoke, motion detectors), actuators (relays, servo motors), and gateways (Raspberry Pi, Arduino, ESP32, PLCs).
Attack Taxonomy
Includes 14 distinct threats categorized into DoS/DDoS attacks (TCP SYN flood, UDP flood, HTTP flood, Slowloris), information gathering (port scanning, OS fingerprinting), man-in-the-middle attacks (ARP spoofing, DNS poisoning, SSL stripping), injection attacks (SQL, command, XSS), and malware attacks (backdoors, ransomware, firmware uploads).
Feature Engineering
From 1,176 initial features, 61 high-correlation features were selected using advanced correlation analysis, significantly reducing dimensionality while maintaining detection accuracy exceeding 99% for binary classification.
View Data Structure
To explore column names, data types, and sample rows, visit the official dataset page on University.
Preview on UniversityCite This Dataset
The dataset creators ask users of this dataset to cite the accompanying paper. Use one of the verified formats below.
Ferrag, M. A., Friha, O., Hamouda, D., Maglaras, L., & Janicke, H. (2022). Edge-IIoTset: A new comprehensive realistic cyber security dataset of IoT and IIoT applications for centralized and federated learning. IEEE Access, 10, 40281--40306. https://doi.org/10.1109/ACCESS.2022.3165809
Source metadata: IEEE (2022) · DOI: 10.1109/ACCESS.2022.3165809
Indexed by IoTDataset.com on Jan 22, 2026
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