Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Comparative Analysis and Development of Recommendations for the Use of Machine Learning Methods to Identify Network Traffic Anomalies in the Development of a Subsystem for User Behavioral Analysis Subject The topic of the resource Machine learning method; Network anomaly detection methods; Network data; Performance evaluation criteria; Traffic anomalies Description An account of the resource This article discusses various machine learning methods in order to conduct a more effective analysis of user network traffic using a subsystem for analyzing user behavior and detecting network anomalies, since there is a need to evaluate big data. The methods and techniques used to detect network anomalies are analyzed. In analyzing the methods and technologies used to detect network anomalies, a classification of anomaly detection methods is proposed. To solve these problems, different algorithms can be used, differing in specificity and, as a result, efficiency. The classification of machine learning methods for detecting network anomalies is considered separately, since machine learning algorithms will be the most effective for the task. Various criteria for evaluating the effectiveness of machine learning models in solving the problem of network traffic profiling are considered. In accordance with the specifics of the tasks of user recognition and network anomaly detection, the most appropriate criteria for evaluating the effectiveness of machine learning models have been selected: AUC ROC the area under the error curve. Four stages of the subsystem for analyzing user behavior and detecting network anomalies are highlighted. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. Creator An entity primarily responsible for making the resource Govorova S.; Govorov E.; Lapin V.; Mary Anita E.A. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-1207 LNNS, pp. 74-84. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-031-77229-0_8" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-77229-0_8</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210153176&amp;doi=10.1007%2F978-3-031-77229-0_8&amp;partnerID=40&amp;md5=d675477b99abea878a57dc670e7d1fa0" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210153176&amp;doi=10.1007%2f978-3-031-77229-0_8&amp;partnerID=40&amp;md5=d675477b99abea878a57dc670e7d1fa0</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-303177228-3 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Govorova S., North Caucasus Federal University, Stavropol, Russian Federation; Govorov E., North Caucasus Federal University, Stavropol, Russian Federation; Lapin V., North Caucasus Federal University, Stavropol, Russian Federation; Mary Anita E.A., CHRIST (Deemed to Be University), Bangalore, India