Labelled attack traffic for intrusion detection research: DDoS and botnet captures, MQTT and protocol abuse, malware traces and federated IDS benchmarks.
Large-scale labeled network traffic captures (PCAPs) from IoT devices. Based on Stratosphere Laboratory's famous IoT-23 dataset with botnet and normal traffic patterns.
Industry-standard dataset for prognostics research with simulated run-to-failure data from 100 turbofan engines including 21 sensor readings and remaining useful life (RUL) labels for predictive maintenance algorithms.
The most cited cybersecurity dataset worldwide with 2.8+ million network flows capturing 14 types of realistic attack scenarios including DDoS, brute force, botnet, and web attacks alongside benign traffic for advanced intrusion detection systems.
Comprehensive IoT network traffic dataset from an academic environment with 202,085 labeled records capturing benign and malicious activities for cybersecurity and anomaly detection research.
Realistic network traffic data containing benign activities and common IoT-targeted attacks like Brute Force, DoS, and Infiltration.
Comprehensive dataset from Stratosphere Laboratory containing network traffic from 23 IoT malware captures including Mirai and Torii botnets, with over 325 million labeled connections for cybersecurity research and ML-based threat detection.
Comprehensive dataset from University of New Brunswick containing over 202,085 network traffic records from IoT devices in academic environment, with classification of benign and malicious activities.
105 IoT devices with 33 attack types (DDoS, DoS, Recon, Brute Force, Spoofing, Mirai) from Canadian Institute for Cybersecurity.
123,117 network flow records with 83 features from real IoT devices (MQTT, ThingSpeak, Wipro) including DDoS, SSH brute-force, and Nmap attacks for IDS model training.