ArticleData in brief2025
MQTTEEB-D: A Real-World IoT Cybersecurity Dataset for AI-Powered Threat Detection in MQTT Networks.
Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Enhancement of cryptography algorithms for security of cloud-based IoT with machine learning models.Scientific reports · 2026Article
- Intelligent defense at the edge: a comprehensive survey of federated learning, TinyML and explainable AI for intrusion detection in IoT and IIoT ecosystems.Frontiers in artificial intelligence · 2026Review
- Attention-enhanced deep learning model for MQTT intrusion detection in internet of medical things.Frontiers in artificial intelligence · 2026Article
- ISAAF: an IoT security and attack prevention framework using AI-driven predictive analytics.Scientific reports · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we introduce the framework and its experimental design, which was used to elaborate the MQTTEEB-D dataset and to execute real-time MQTT-based attacks while collecting traffic data. The MQTTEEB-D dataset is a practical real-world data set for intrusion detection improvement in MQTT-based IoT networks. Unlike already existing datasets, which are constructed using simulated network traffic, MQTTEEB-D is obtained from a real-time IoT testbed, named MQTTEEB. Various cyberattacks, including Denial of Service (DoS), Slow DoS against Internet of Things Environments (SlowITe), Malformed Data Injection, Brute Force, and MQTT publish flooding, were carried out in real-time while allowing close monitoring of network traffic anomalies. Data was gathered using PyShark and organized into multiple CSV files. To ensure high data quality, we performed pre-processing steps, such as outlier removal, normalization, standardization, and class balance. Several processed forms of data (e.g., raw, cleaned, normalized, standardized), along with detailed metadata are also provided for being used, for instance, to develop and validate AI-driven intrusion detection models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.