Evaluation of the Limit of Detection in Network Dataset Quality Assessment with PerQoDA Wasielewska, Katarzyna Soukup, Dominik Cejka, Tomas Camacho Páez, José Dataset quality assessment Permutation testing Network dataset Network security Attack detection Machine learning Classification Machine learning is recognised as a relevant approach to detect attacks and other anomalies in network traffic. However, there are still no suitable network datasets that would enable effective detection. On the other hand, the preparation of a network dataset is not easy due to privacy reasons but also due to the lack of tools for assessing their quality. In a previous paper, we proposed a new method for data quality assessment based on permutation testing. This paper presents a parallel study on the limits of detection of such an approach. We focus on the problem of network flow classification and use well-known machine learning techniques. The experiments were performed using publicly available network datasets. 2023-04-24T07:50:11Z 2023-04-24T07:50:11Z 2022 info:eu-repo/semantics/conferenceObject https://hdl.handle.net/10481/81204 eng http://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/openAccess Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2022, 4th Workshop on Machine Learning for Cybersecurity (MLCS)