The dataset was systematically developed by augmenting and refining the Aposemat-Bot-IoT-23 dataset to address limitations in class imbalance, labeling consistency, and feature representation. Unlike prior datasets that include limited or uneven distributions of malware families, this dataset focuses on high-quality botnet traffic and benign behavior, ensuring reliable and scalable modeling of IoT botnet activities. It […]
The dataset was systematically developed by integrating and augmenting multiple high-quality MQTT-based intrusion detection datasets, enabling a comprehensive and protocol-aware representation of IoT communication. Unlike prior datasets that predominantly focus on packet-level or TCP-based analysis with limited consideration of application-layer semantics, this dataset captures rich MQTT behavioral patterns by leveraging protocol-aware feature extraction and diverse […]
The BCCC-IoT-IDS-ZWave-2025 Dataset is a large-scale, multi-source IoT security dataset developed over five months (20 TB data, including more than 1 BILLION records) using a comprehensive smart-home testbed comprising more than 110 devices, including sensors, actuators, smart plugs, locks, meters, and controllers. According to the paper, the dataset includes 88 distinct attack scenarios spanning network-layer, […]
The CIC-BCCC-NRC TabularIoTAttack-2024 dataset is a comprehensive collection of IoT network traffic data generated as part of an advanced effort to create a reliable source for training and testing AI-powered IoT cybersecurity models. This dataset is designed to address modern challenges in detecting and identifying IoT-specific cyberattacks, offering a rich and diverse set of labeled […]