Network Traffic Anomalies Identification Based on Classification Methods
Donatas Račys (Vilnius Gediminas Technical University, Lithuania)
Dalius Mažeika (Vilnius Gediminas Technical University, Lithuania)
Dalius Mažeika (Vilnius Gediminas Technical University, Lithuania)
Abstract
A problem of network traffic anomalies detection in the computer networks is analyzed. Overview of anomalies detection methods is given then advantages and disadvantages of the different methods are analyzed. Model for the traffic anomalies detection was developed based on IBM SPSS Modeler and is used to analyze SNMP data of the router. Investigation of the traffic anomalies was done using three classification methods and different sets of the learning data. Based on the results of investigation it was determined that C5.1 decision tree method has the largest accuracy and performance and can be successfully used for identification of the network traffic anomalies.
Article in:
Lithuanian
Article published:
2015-07-13
Keyword(s): anomalies detection; classification methods; computer network.
DOI: 10.3846/mla.2015.796
Science – Future of Lithuania / Mokslas – Lietuvos Ateitis ISSN 2029-2341, eISSN 2029-2252
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 License.