Detail výsledku
Machine Learning Metrics for Network Datasets Evaluation
High-quality datasets are an essential requirement for leveraging machine learning (ML) in data processing and recently in network security as well. However, the quality of datasets is overlooked or underestimated very often. Having reliable metrics to measure and describe the input dataset enables the feasibility assessment of a dataset. Imperfect datasets may require optimization or updating, e.g., by including more data and merging class labels. Applying ML algorithms will not bring practical value if a dataset does not contain enough information. This work addresses the neglected topics of dataset evaluation and missing metrics. We propose three novel metrics to estimate the quality of an input dataset and help with its improvement or building a new dataset. This paper describes experiments performed on public datasets to show the benefits of the proposed metrics and theoretical definitions for more straightforward interpretation. Additionally, we have implemented and published Python code so that the metrics can be adopted by the worldwide scientific community.
Class labels; Feasibility assessment; High quality; In networks; Learning metrics; Machine learning algorithms; Machine-learning; Networks security; Optimisations; Public dataset
@inproceedings{BUT193581,
author="Dominik {Soukup} and Daniel {Poliakov} and Tomáš {Čejka} and {}",
title="Machine Learning Metrics for Network Datasets Evaluation",
booktitle="IFIP International Conference on ICT Systems Security and Privacy Protection",
year="2024",
pages="307--320",
publisher="Springer International Publishing",
address="Poznan",
doi="10.1007/978-3-031-56326-3\{_}22",
isbn="978-3-031-56325-6"
}