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Conformal Clustering and Its Application to Botnet Traffic

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Statistical Learning and Data Sciences (SLDS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9047))

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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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References

  1. Elfeky, M.G., Aref, W.G., Elmagarmid, A.K.: Periodicity detection in time series databases. IEEE Transactions on Knowledge and Data Engineering 17(7), 875–887 (2005)

    Article  Google Scholar 

  2. Enders, W.: Applied econometric time series (1995)

    Google Scholar 

  3. Gammerman, A., Vovk, V.: Hedging predictions in machine learning. The Computer Journal 50(2), 151–163 (2007)

    Article  Google Scholar 

  4. Laxhammar, R., Falkman, G.: Sequential conformal anomaly detection in trajectories based on hausdorff distance. In: 2011 Proceedings of the 14th International Conference on Information Fusion (FUSION), pp. 1–8. IEEE (2011)

    Google Scholar 

  5. Lei, J., Rinaldo, A., Wasserman, L.: A conformal prediction approach to explore functional data. Annals of Mathematics and Artificial Intelligence, pp. 1–15 (2013)

    Google Scholar 

  6. Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9(2579–2605), 85 (2008)

    Google Scholar 

  7. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to information retrieval, vol. 1. Cambridge University Press, Cambridge (2008)

    Book  MATH  Google Scholar 

  8. Smith, J., Nouretdinov, I., Craddock, R., Offer, C., Gammerman, A.: Anomaly Detection of Trajectories with Kernel Density Estimation by Conformal Prediction. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H., Sioutas, S., Makris, C. (eds.) Artificial Intelligence Applications and Innovations. IFIP AICT, vol. 437, pp. 271–280. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  9. Tegeler, F., Fu, X., Vigna, G., Kruegel, C.: Botfinder: Finding bots in network traffic without deep packet inspection. In: Proceedings of the 8th International Conference on Emerging Networking Experiments and Technologies, pp. 349–360. ACM (2012)

    Google Scholar 

  10. Vovk, V., Gammerman, A., Shafer, G.: Algorithmic learning in a random world. Springer (2005)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Learning Research Centre and Computer Science Department, Royal Holloway University of London, Egham Hill, Egham, Surrey, TW20 OEX, UK

    Giovanni Cherubin, Ilia Nouretdinov & Alexander Gammerman

  2. Systems Security Research Lab and Information Security Group, Royal Holloway University of London, Egham Hill, Egham, Surrey, TW20 OEX, UK

    Giovanni Cherubin, Roberto Jordaney, Zhi Wang, Davide Papini & Lorenzo Cavallaro

Authors
  1. Giovanni Cherubin
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  2. Ilia Nouretdinov
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  3. Alexander Gammerman
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  4. Roberto Jordaney
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  5. Zhi Wang
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  6. Davide Papini
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  7. Lorenzo Cavallaro
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Corresponding author

Correspondence to Giovanni Cherubin .

Editor information

Editors and Affiliations

  1. University of London, Egham, Surrey, United Kingdom

    Alexander Gammerman

  2. University of London, Egham, Surrey, United Kingdom

    Vladimir Vovk

  3. Frederick University, Nicosia, Cyprus

    Harris Papadopoulos

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© 2015 Springer International Publishing Switzerland

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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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