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 data that reflects realistic IoT network behaviours. The dataset extracted a wide array of network characteristics using CICFlowMeter, with each record containing relevant features such as network flows, timestamps, source/destination IPs, and attack labels.
The full research paper outlining the details of the dataset and its underlying principles:
"An Efficient Self Attention-Based 1D-CNN-LSTM Network for IoT Attack Detection and Identification Using Network Traffic”, Tinshu Sasi, Arash Habibi Lashkari, Rongxing Lu, Pulei Xiong, Shahrear Iqbal, Journal of Information and Intelligence, 2024, ISSN 2949-7159, https://doi.org/10.1016/j.jiixd.2024.09.001
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