derived CICIoT 2023 datasets

These datasets is derived from the publicly available CIC IoT 2023 described in: DOI: https://doi.org/10.3390/s23135941. The original data are available at: https://www.unb.ca/cic/datasets/iotdataset-2023.html
These derived datasets are intended to support research on intrusion detection using machine learning and deep learning models.
The two datasets included are:

Binary Classification Dataset (binary_data)
This version groups all attack types into a single “Attack” class and retains the normal traffic as a separate “Benign” class. It is designed to evaluate binary intrusion detection models.

Eight-Class Classification Dataset (8class_data)
This version preserves a more detailed categorization of attacks by organizing them into eight meaningful classes. This allows researchers to study multi-class intrusion detection performance in more complex environments.ttps://www.unb.ca/cic/datasets/iotdataset-2023.html

本数据集衍生自公开可用的CIC IoT 2023数据集，相关文献信息为：DOI: https://doi.org/10.3390/s23135941。原始数据集可通过以下网址获取：https://www.unb.ca/cic/datasets/iotdataset-2023.html。该衍生数据集旨在支持基于机器学习与深度学习模型的入侵检测研究。本次包含两类衍生数据集：1. 二元分类数据集（Binary Classification Dataset，binary_data）：该版本将所有攻击类型统一归为「攻击（Attack）」类别，并将正常流量单独划分为「良性（Benign）」类别，用于评估二元入侵检测模型。2. 八类分类数据集（Eight-Class Classification Dataset，8class_data）：该版本保留了更精细化的攻击分类体系，将攻击划分为八个具有实际意义的类别，可支持研究人员在更复杂的场景下开展多分类入侵检测性能相关研究。