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Conformal Clustering and Its Application to Botnet Traffic

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Statistical Learning and Data Sciences (SLDS 2015)

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.

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Correspondence to Giovanni Cherubin .

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Cherubin, G. et al. (2015). Conformal Clustering and Its Application to Botnet Traffic. In: Gammerman, A., Vovk, V., Papadopoulos, H. (eds) Statistical Learning and Data Sciences. SLDS 2015. Lecture Notes in Computer Science(), vol 9047. Springer, Cham. https://doi.org/10.1007/978-3-319-17091-6_26

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  • DOI: https://doi.org/10.1007/978-3-319-17091-6_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17090-9

  • Online ISBN: 978-3-319-17091-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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