Recent research on Internet traffic classification has achieved certain success in the application of machine learning techniques into flow statistics based method. However, existing methods fail to deal with zero-day traffic which are generated by previously unknown applications in a traffic classification system. To tackle this critical problem, we propose a novel traffic classification scheme which has the capability of identifying zero-day traffic as well as accurately classifying the traffic generated by pre-defined application classes. In addition, the proposed scheme provides a new mechanism to achieve fine-grained classification of zero-day traffic through manually labeling very few traffic flows. The preliminary empirical study on a big traffic data show that the proposed scheme can address the problem of zero-day traffic effectively. When zero-day traffic present, the classification performance of the proposed scheme is significantly better than three state-of-the-art methods, random forest classifier, classification with flow correlation, and semi-supervised traffic classification. © Springer International Publishing Switzerland 2013.
CITATION STYLE
Zhang, J., Chen, X., Xiang, Y., & Zhou, W. (2013). Cyberspace Safety and Security. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8300, pp. 213–227). Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84894220623&partnerID=tZOtx3y1
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