Conformal Clustering and Its Application to Botnet Traffic. / Cherubin, Giovanni; Nouretdinov, Ilia; Gammerman, Alex; Jordaney, Roberto; Wang, Zhi; Papini, Davide; Cavallaro, Lorenzo.

Statistical Learning and Data Sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings. ed. / Alex Gammerman; Vladimir Vovk; Harris Papadopoulos. Springer, 2015. p. 313-322 (Lecture Notes in Computer Science; Vol. 9047).

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


This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 24468158