Abstract
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.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
