Conformal Clustering and Its Application to Botnet Traffic

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

doi:10.1007/978-3-319-17091-6_26

Conformal Clustering and Its Application to Botnet Traffic

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

Informatics

Research output: Contribution to journal › Conference paper › peer-review

14 Citations (Scopus)

Abstract

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 language	English
Pages (from-to)	313-322
Journal	Statistical Learning and Data Sciences
Volume	9047
DOIs	https://doi.org/10.1007/978-3-319-17091-6_26
Publication status	Published - 3 Apr 2015

Access to Document

10.1007/978-3-319-17091-6_26

Cite this

@article{9d22a18e69f84925bf9b864e9f8d8649,

title = "Conformal Clustering and Its Application to Botnet Traffic",

abstract = "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.",

author = "Giovanni Cherubin and Ilia Nouretdinov and Alexander Gammerman and Roberto Jordaney and Zhi Wang and Davide Papini and Lorenzo Cavallaro",

year = "2015",

month = apr,

day = "3",

doi = "10.1007/978-3-319-17091-6_26",

language = "English",

volume = "9047",

pages = "313--322",

journal = "Statistical Learning and Data Sciences",

issn = "0302-9743",

publisher = "Springer",

}

TY - JOUR

T1 - Conformal Clustering and Its Application to Botnet Traffic

AU - Cherubin, Giovanni

AU - Nouretdinov, Ilia

AU - Gammerman, Alexander

AU - Jordaney, Roberto

AU - Wang, Zhi

AU - Papini, Davide

AU - Cavallaro, Lorenzo

PY - 2015/4/3

Y1 - 2015/4/3

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-319-17091-6_26

DO - 10.1007/978-3-319-17091-6_26

M3 - Conference paper

SN - 0302-9743

VL - 9047

SP - 313

EP - 322

JO - Statistical Learning and Data Sciences

JF - Statistical Learning and Data Sciences

ER -

Conformal Clustering and Its Application to Botnet Traffic

Abstract

Access to Document

Fingerprint

Cite this