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41–52 (2006) 2. : Performance debugging for distributed systems of black boxes. In: Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles, pp. 74–89 (2003) 3. : An analysis of the slapper worm. IEEE Security and Privacy 1(1), 82–87 (2003) 4. : A survey of clustering data mining techniques. In: Grouping Multidimensional Data, pp. 25–71. Springer, Heidelberg (2006) 5. : The fog of cyberwar – to defend... , http://www. ch/isn/Current-Affairs/Special-Reports/The-Fog-of-Cyberwar/ Botnets/ (accessed on 08/30/10) 6.
Rev. 38(4), 171–182 (2008) 39. : A fast parallel clustering algorithm for large spatial databases. Data Min. Knowl. Discov. au Abstract. Entropy has been widely used for anomaly detection in various disciplines. One such is in network attack detection, where its role is to detect significant changes in underlying distribution shape due to anomalous behaviour such as attacks. In this paper, we point out that entropy has significant blind spots, which can be made use by adversaries to evade detection.
Xi )) >≥ ρ − ξi , ξi ≥ 0 (2) The optimization problem has to be solved to identify two variables: w and ρ. ξi variables represent slack variables for allowing some points of S to not be located on the right side of the hyperplane. This avoids to bias the problem with very particular and maybe erroneous points. Since defining a projection function is not obvious, support vector methods traditionally rely on kernel functions. From a general point of view, they can be considered as similarity measure which have to be finitely positive semi-definite functions.