BCCC-IoT-IDS-Zwave-2025 is a behavior-centric cybersecurity dataset focusing on Z-wave protocol vulnerabilities and intrusion detection for modern smart home automation systems.
A 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.
Dataset documents replay attacks targeting MQTT communications in water distribution system with 4.8 MB CSV file containing original and replayed messages for temporal sequence analysis.
IoT-23 provides labeled IoT network-traffic captures, including 20 malware scenarios and 3 benign IoT captures, intended to support machine-learning research on IoT security.
A 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.
Released in October 2025, this dataset captures performance metrics and network traffic associated with implementing Post-Quantum Cryptography (PQC) in Industrial IoT (IIoT) scenarios. It supports research into the feasibility and overhead of quantum-resistant security protocols on resource-constrained industrial hardware.
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.
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.
Comprehensive Industrial IoT security dataset from the Canadian Institute for Cybersecurity, featuring realistic network traffic with 34 types of attacks including DDoS, ransomware, data exfiltration, and advanced persistent threats across multiple IIoT protocols.
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.