RT-IoT2022 is a network traffic dataset specifically derived from a real-time IoT testbed containing smart home devices. It includes both normal traffic and various common network attacks.
The Gotham Dataset is a large-scale, reproducible benchmark for evaluating decentralized Intrusion Detection Systems (IDS) and Federated Learning in virtualized smart cities. It captures interface-level network traffic from 78 heterogeneous IoT devices, including complex attack vectors like Mirai botnets, Merlin C2 traffic, and CoAP amplification, preserving the non-IID nature of edge data for realistic AI security training.
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.
DataSense is a real-time Industrial IoT (IIoT) dataset from the Canadian Institute for Cybersecurity, combining synchronized sensor and network data from a 40-device testbed with over 15 types of industrial sensors for anomaly and intrusion detection research.
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.
Deep learning-ready dataset combining real vehicle CAN bus traffic and simulated attack scenarios (DoS, fuzzing, spoofing) for training intrusion detection systems to protect autonomous and connected vehicles from cyber-attacks.
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.