MQTTEEB-D: Real-World IoT Cybersecurity Dataset for AI-Powered Threat Detection in MQTT Networks
Catalog Summary
"A real-world cybersecurity dataset capturing MQTT-based IoT network traffic with live attacks and anomalous behavior. Collected from an active deployment with multiple attack types including DoS, SlowITe, and malformed injections. Provides both raw and preprocessed CSV files with rich metadata for intrusion detection and anomaly classification research."
Catalog Notes
Overview
This dataset captures live MQTT IoT communication under both benign and malicious conditions, designed to improve threat detection models. Attacks include Denial of Service, SlowITe, and malformed payloads.
Dataset Contents
- Data modality: Time-series CSV sensor and network data
- Scale: Multiple attack scenarios, real deployment
- File formats: CSV, multiple processed versions
- Metadata: Timestamps, node IDs, attack labels
Collection Methodology
- Environment: Real IoT deployment
- Duration: Continuous with attacks
- Devices/Sensors: Health sensors with MQTT
- Sampling rate: Configurable during capture
Labels and Targets
- Labeling: Attack categories vs normal
- Ground truth: Controlled test scenarios
Recommended Use Cases
- ML-based threat detection
- Anomaly classification
- Security research in MQTT environments
Limitations and Considerations
- Deployment complexity
- Requires preprocessing for some models
Access and Licensing
This dataset is available at official repository page. Licensed under CC BY 4.0.
View Data Structure
To explore column names, data types, and sample rows, visit the official dataset page on Mendeley Data.
Preview on Mendeley DataCite This Dataset
Aqachtoul, A., Karam, K., Elamrani, A., Najib, M., Rafalia, N., & Bakhouya, M. (2025). MQTTEEB-D: Real-World IoT Cybersecurity Dataset for AI-Powered Threat Detection in MQTT Networks. [Dataset]. Mendeley Data. https://doi.org/10.17632/jfttfjn6tr.1
Source metadata: Mendeley Data (2025) · DOI: 10.17632/jfttfjn6tr.1
Indexed by IoTDataset.com on Feb 07, 2026
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