Loading [a11y]/accessibility-menu.js
Internet traffic classification based on Min-Max Ensemble Feature Selection | IEEE Conference Publication | IEEE Xplore

Internet traffic classification based on Min-Max Ensemble Feature Selection


Abstract:

Internet traffic classification is one of the key foundations for research works and traffic engineering in Internet. With the rapid increase of Internet applications and...Show More

Abstract:

Internet traffic classification is one of the key foundations for research works and traffic engineering in Internet. With the rapid increase of Internet applications and the number of Internet flow, the technique challenges are coupled with development of traffic classification all the time. Currently, the machine learning-based technique has attracted much attention, since it can address the issues that the usage of the dynamic port numbers and the encryption technique at the transport layer in traffic. As we have known, feature selection is one of the key problems in machine learning. In this paper, in order to improve the efficiency of feature selection in dealing with large scale traffic data problem, especially to imbalance classification problem that occur in traffic classification, a Min-Max Ensemble Feature Selection (M2-EFS) is proposed to deal with traffic data, which based on balanced data partition and min-max ensemble strategy. The experimental results demonstrate that the M2-EFS can obtain higher performance in most cases, and it could efficiently deal with imbalanced problems.
Date of Conference: 24-29 July 2016
Date Added to IEEE Xplore: 03 November 2016
ISBN Information:
Electronic ISSN: 2161-4407
Conference Location: Vancouver, BC, Canada

References

References is not available for this document.