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Conformal Clustering and Its Application to Botnet Traffic

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Statistical Learning and Data Sciences (SLDS 2015)


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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  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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Correspondence to Giovanni Cherubin .

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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.

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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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