Reliable Probabilistic Classification of Internet Traffic

Mikhail Dashevskiy, Zhiyuan Luo

Research output: Contribution to journalArticlepeer-review


Classification of Internet traffic is very important to many applications such as network resource management, network security enforcement and intrusion detection. Many machine-learning algorithms have been successfully used to classify network traffic flows with good performance, but without information about the reliability in classifications. In this paper, we present a recently developed algorithmic framework, namely the Venn Probability Machine, for making reliable decisions under uncertainty. Experiments on publicly available real Internet traffic datasets show the algorithmic framework works well. Comparison is also made to the published results.
Original languageEnglish
Pages (from-to)133-146
Number of pages14
JournalInternational Journal of Information Acquisition
Issue number2
Publication statusPublished - 2009

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