IoT botnet IDS dataset using 83 real and emulated devices with Mirai, BashLite, and Torii traffic. Raw PCAP files support botnet and anomaly detection research.
Real IoT botnet traffic dataset from 9 commercial devices (webcams, routers, thermostats) authentically infected by Mirai and BASHLITE. Over 7M records, 115 statistical features. CSV format. Benchmark for deep-learning-based IoT anomaly and botnet detection.
Large-scale IoT cybersecurity dataset with 47M+ labeled network flows from 105 real IoT devices across 33 attack types in 7 categories. PCAP and CSV formats. Built for IDS/IPS development and ML-based IoT traffic classification research.
Real IoT malware traffic dataset with 325M labeled network flows from 20 malware and 3 benign device captures over 500+ hours. PCAP and Zeek conn.log formats. Used for IoT botnet detection, malware traffic classification, and ML security research.
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.
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.
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.
Comprehensive large-scale IoT botnet dataset combining legitimate IoT network traffic with realistic botnet attack scenarios. Features full packet captures (PCAP) and extracted flow features for diverse attack types including DDoS, reconnaissance, theft, and DoS attacks.
Comprehensive large-scale IoT intrusion detection dataset from Canadian Institute for Cybersecurity with 33 attack types across 105 real IoT devices. Includes 8.94 GB of network traffic data covering DDoS, DoS, Mirai, MITM, and reconnaissance attacks.
New realistic IoT network intrusion dataset (MU-IoT) with comprehensive attack scenarios for cybersecurity research. Published in IEEE 2024 with 4+ citations. Covers multiple IoT protocols and device types.