By Jérôme François, Shaonan Wang, Radu State, Thomas Engel (auth.), Jordi Domingo-Pascual, Pietro Manzoni, Sergio Palazzo, Ana Pont, Caterina Scoglio (eds.)
The two-volume set LNCS 6640 and 6641 constitutes the refereed lawsuits of the tenth foreign IFIP TC 6 Networking convention held in Valencia, Spain, in may well 2011. The sixty four revised complete papers awarded have been rigorously reviewed and chosen from a complete of 294 submissions. The papers function cutting edge examine within the parts of functions and companies, subsequent new release net, instant and sensor networks, and community technology. the 1st quantity comprises 36 papers and is equipped in topical sections on anomaly detection, content material administration, DTN and sensor networks, strength potency, mobility modeling, community technology, community topology configuration, subsequent new release web, and course diversity.
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Additional info for NETWORKING 2011: 10th International IFIP TC 6 Networking Conference, Valencia, Spain, May 9-13, 2011, Proceedings, Part I
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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.