Conformal Clustering and Its Application to Botnet Traffic

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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
Title of host publicationStatistical Learning and Data Sciences
Subtitle of host publicationThird International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings
EditorsAlex Gammerman, Vladimir Vovk, Harris Papadopoulos
Number of pages10
ISBN (Electronic)978-3-319-17091-6
ISBN (Print)978-3-319-17090-9
Publication statusPublished - 3 Apr 2015

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
  • Best student paper

    Cherubin, Giovanni (Recipient), 2015

    Prize: Prize (including medals and awards)

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