Detecting anomalies and attacks in network traffic monitoring with classification methods and XAI-based explainability

Autor:	Mariusz Uchroński, Marcin Michalak, Andrzej Bialas, Rafał Kurianowicz, Adrian Kajzer, Łukasz Wawrowski, Marek Sikora
Rok vydání:	2021
Předmět:	Data set Binary classification Computer science Benchmark (computing) General Earth and Planetary Sciences Classification methods Data mining computer.software_genre computer General Environmental Science Safety monitoring
Zdroj:	KES
ISSN:	1877-0509
DOI:	10.1016/j.procs.2021.08.239
Popis:	Assuring the network traffic safety is a very important issue in a variety of today’s industries. Therefore, the development of anomalies and attacks detection methods has been the goal of analyses. In the paper the binary classification-based approach to network traffic safety monitoring is presented. The well known methods were applied to artificially modified network traffic data and their detection capabilities were tested. More detailed interpretation of the nature of detected anomalies is carried out with the help of the XAI approach. For the purpose of experiments a new benchmark network traffic data set was prepared, which is now commonly available.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b6bb2b201ee91ebf7caae3e25f56150a https://doi.org/10.1016/j.procs.2021.08.239 Zobrazit plný text záznamu