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MQTT_UAD: MQTT Under Attack Dataset. A public dataset for the detection of attacks in IoT networks using MQTT protocol.

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CoAP_UAD: CoAP under attack dataset - A comprehensive dataset for CoAP-based IoT security research.

Jose Aveleira-Mata1, Álvaro Michelena2, Isaías García-Rodríguez1

  • 1University of León, Department of Electric, Systems and Automatics Engineering, León, Spain.

Data in Brief
|November 11, 2025
PubMed
Summary

A new dataset, CoAP_UAD, aids in detecting attacks targeting the Constrained Application Protocol (CoAP) in Internet of Things (IoT) networks. This resource facilitates research into securing resource-constrained devices against protocol-level threats.

Keywords:
Anomaly detectionConstrained application protocol (CoAP)Cyberattack datasetIntrusion detection systems (IDS)IoT securityIoT traffic analysisProtocol-level attacks

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Area of Science:

  • Cybersecurity
  • Internet of Things (IoT)
  • Network Protocols

Background:

  • Lightweight protocols like the Constrained Application Protocol (CoAP) are crucial for resource-constrained Internet of Things (IoT) devices.
  • However, their design and reliance on UDP expose CoAP networks to unique protocol-level attacks.
  • Effective intrusion detection is vital for maintaining the reliability and safety of these IoT deployments.

Purpose of the Study:

  • To introduce CoAP_UAD, a novel, publicly available dataset for studying and benchmarking intrusion detection systems for CoAP-based IoT networks.
  • To provide a standardized resource for evaluating defenses against protocol-semantic attacks, abstracting away device-specific vulnerabilities.

Main Methods:

  • Developed a realistic testbed emulating constrained IoT devices and CoAP deployments.
  • Executed diverse protocol-oriented attacks, including cross-protocol interaction, message manipulation, and Denial of Service (Block size amplification).
  • Captured network traffic at the router, labeling each frame/flow and exporting data in CSV format for reproducibility.

Main Results:

  • Created CoAP_UAD, a dataset featuring labeled network traffic from a realistic IoT testbed.
  • The dataset includes various attacks specifically targeting CoAP protocol behaviors.
  • Data is formatted in CSV for ease of use in intrusion detection system research.

Conclusions:

  • CoAP_UAD offers a valuable resource for advancing research in IoT network security.
  • The dataset enables the development and benchmarking of intrusion detection methods tailored for CoAP.
  • It supports the creation of more robust and secure constrained IoT environments.