Conformal Clustering and Its Application to Botnet Traffic

Giovanni Cherubin, Ilia Nouretdinov, Alexander Gammerman, Roberto Jordaney, Zhi Wang, Davide Papini, Lorenzo Cavallaro

Research output: Contribution to journalConference paperpeer-review

17 Citations (Scopus)


The paper describes an application of a novel clustering technique based on Conformal Predictors. Unlike traditional clustering methods, this technique allows to control the number of objects that are left outside of any cluster by setting up a required confidence level. This paper considers a multi-class unsupervised learning problem, and the developed technique is applied to bot-generated network traffic. An extended set of features describing the bot traffic is presented and the results are discussed.
Original languageEnglish
Pages (from-to)313-322
JournalStatistical Learning and Data Sciences
Publication statusPublished - 3 Apr 2015


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