MQTT_UAD is a public MQTT traffic dataset published in Data in Brief 2025, containing labeled benign and attack scenarios in IoT networks that use the MQTT protocol, designed for training and evaluating intrusion detection systems.
An MQTT DoS and DDoS IoT attack dataset collected on a Raspberry Pi 3B+ Mosquitto broker over 12 sessions, including three days of normal traffic and several minutes of attack traffic, totaling 424,716 labeled entries for machine learning-based IDS and IPS research.
A real-world MQTT-based IoT cybersecurity dataset collected from the MQTTEEB testbed at the International University of Rabat, with benign traffic and five attack types (DoS, SlowITe, Malformed Data Injection, Brute Force, Publish Flooding), provided in multiple processed forms (raw, cleaned, normalized, standardized, SMOTE) for AI-driven intrusion detection research.
A merged and optimized dataset combining N-BaIoT (IoT-specific traffic) and UNSW-NB15 (general network threats) with feature engineering, dimensionality reduction, and benchmarked ML models (Decision Tree, SVM, Random Forest, Neural Network) for IoT anomaly detection, published in Scientific Reports.
A federated learning evaluation across several contemporary IoT and IIoT intrusion detection datasets, benchmarking algorithms such as FedAvg, FedProx, and FedNova with LSTM and Transformer models in in-domain, cross-dataset, and multi-dataset federation scenarios.
IoT-DH is a real-world IoT DDoS honeypot dataset collected from a honeypot deployment and converted from PCAP to CSV with traffic features and labels for DDoS classification, identification, and detection tasks.
Multimodal dataset combining Text-to-SQL natural language queries with IoT network traffic classification, featuring 10,985 SQL training examples and labeled network traffic (benign/malicious) from IoT-23 and Smart Building sensors for NLP and security research.
Comprehensive collection of research papers on IoT attacks and security models published between 2005-2025 in IEEE, Elsevier, Springer, ACM, and MDPI, compiled for systematic review of IoT network security across all layers, enabling meta-analysis and trend identification.
Large-scale reproducible IoT network dataset with traffic from 100+ diverse IoT devices including smart home, wearable, and industrial sensors, featuring multiple attack scenarios and benign behavior for intrusion detection research.
Real-time network traffic dataset from diverse IoT devices including normal behavior and various attacks (DDoS, brute-force, scans) for developing intrusion detection systems.
Dataset for evaluating federated learning approaches to IoT intrusion detection published in Nature Scientific Reports January 2026. Features distributed network traffic from multiple IoT deployments with privacy constraints and decentralized learning evaluation metrics.
Specialized dataset for detecting IoT botnet attacks using network traffic analysis. Captures behavior of 9 real IoT devices infected with Mirai and BASHLITE malware variants. Ideal for training ML models to identify compromised IoT devices through traffic patterns.