Federated Learning for Distributed Intrusion Detection Systems in Public Networks - Validation Dataset
Abstract
This dataset has been meticulously prepared and utilized as a validation set during the evaluation phase of "Meta IDS" to asses the performance of various machine learning models. It is now made available for interested users and researchers who seek a reliable and diverse dataset for training and testing their own custom models. The validation dataset comprises a comprehensive collection of labeled entries, that determines whether the packet type is "malicious" or "benign." It covers complex design patterns that are commonly encountered in real-world applications. The dataset is designed to be representative, encompassing edge and fog layers that are in contact with cloud layer, thereby enabling thorough testing and evaluation of different models. Each sample in the dataset is labeled with the corresponding ground truth, providing a reliable reference for model performance evaluation. To ensure convenient distribution and storage, the dataset has been broken down into three separate batches, each containing a portion of the dataset. This allows for convenient downloading and management of the dataset. The three batches are provided as individual compressed files. In order to extract the data, follow the following instructions: Download and install bzip2 (if not already installed) from the official website or your package manager. Place the compressed dataset file in a directory of your choice. Open a terminal or command prompt and navigate to the directory where the…
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