INDDOS24 - IoT DDoS Attack Dataset
Comprehensive synthetic dataset designed for analyzing DDoS attacks in Internet of Things environments.
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Comprehensive synthetic dataset designed for analyzing DDoS attacks in Internet of Things environments.
View DatasetComprehensive IoT attack dataset for device identification and anomaly detection in security analytics applications.
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 DatasetBCCC-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.
View DatasetA large-scale dataset (245GB) collected from real-world industrial control systems for advanced threat detection.
View DatasetA large-scale, reproducible network dataset for evaluating modern IoT intrusion detection systems.
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 DatasetDataset 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.
View DatasetIoT-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.
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 DatasetReleased 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.
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.
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