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MQTTEEB-D: Real-World IoT Cybersecurity Dataset for AI-Powered Threat Detection in MQTT Networks

Cybersecurity
Feb 07, 2026
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Abstract

"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."

Description

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 Data

Cite 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

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Original source: Mendeley Data (2025). Visit official page for more details.

Indexed by IoTDataset.com on Feb 07, 2026

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