Showing 12 of 51 datasets
Free CC0 synthetic dataset: 500 rows of labelled network flows covering DoS, DDoS, botnet and reconnaissance traffic. 18% Attacks.
View DatasetReproducible large-scale IoT network dataset from 78 emulated devices using MQTT, CoAP, and RTSP protocols. Includes benign and malicious traffic with DoS, brute force, scanning, and C&C attacks in PCAP and CSV formats.
View DatasetLatest 2026 IoT malware dataset from the Canadian Institute for Cybersecurity (CIC) and Yunnan University, featuring comprehensive malware samples and behavioral analysis data for IoT threat detection research.
View DatasetA comprehensive dataset derived from real-time IoT infrastructure, designed for intrusion detection research and network security analysis.
View DatasetComprehensive synthetic dataset designed for analyzing DDoS attacks in Internet of Things environments.
View DatasetToN_IoT is a large-scale dataset featuring heterogeneous data from IoT sensors, operating systems, and network traffic for advanced intrusion detection research in Industry 4.0.
View DatasetA comprehensive and realistic IoT dataset generated by the Canadian Institute for Cybersecurity (CIC) for profiling, detecting, and characterizing multi-vector IoT attacks in a real network topology.
View DatasetA 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.
View DatasetCICIoT2023 is a large-scale, flow-based network traffic dataset capturing real-time benign and malicious communications in an IoT environment composed of 105 physical devices. The dataset captures traffic traces for 33 attack scenarios grouped into seven categories: DDoS, DoS, Reconnaissance, web-based attacks, brute-force attempts, spoofing, and Mirai malware.
View DatasetMQTT_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.
View DatasetA 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.
View DatasetA 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.
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