Sven Dietrich's Detection of Intrusions and Malware, and Vulnerability PDF

By Sven Dietrich

This publication constitutes the refereed lawsuits of the eleventh overseas convention on Detection of Intrusions and Malware, and Vulnerability evaluation, DIMVA 2014, held in Egham, united kingdom, in July 2014.

The thirteen revised complete papers offered including one prolonged summary have been conscientiously reviewed and chosen from 60 submissions. The papers are prepared in topical sections on malware, cellular safety, community protection and host security.

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Read or Download Detection of Intrusions and Malware, and Vulnerability Assessment: 11th International Conference, DIMVA 2014, Egham, UK, July 10-11, 2014. Proceedings PDF

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Extra resources for Detection of Intrusions and Malware, and Vulnerability Assessment: 11th International Conference, DIMVA 2014, Egham, UK, July 10-11, 2014. Proceedings

Sample text

Of the 17th ACM Conference on Computer and Communications Security, CCS 2010 (2010) 37. : Array regrouping and structure splitting using whole-program reference affinity. In: Proc. of the ACM SIGPLAN 2004 Conference on Programming Language Design and Implementation, PLDI 2004(2004) Identifying Shared Software Components to Support Malware Forensics Brian Ruttenberg1 , Craig Miles2 , Lee Kellogg1, Vivek Notani2 Michael Howard1 , Charles LeDoux2 , Arun Lakhotia2 , and Avi Pfeffer1 1 Charles River Analytics Cambridge, MA, USA 2 Software Research Lab University of Louisiana at Lafayette Lafayette, LA, USA Abstract.

The intuition is that after the first stage of clustering, there are clusters C1 . . C|T| that each contain the set of procedures pi,∗ . That is, from procedure uniqueness and a reasonable clustering algorithm, it is highly likely that we get |T| clusters, one for each component in the data set. Identifying Shared Software Components to Support Malware Forensics 31 Using Thm. 1 and procedure uniqueness, we can now state the following corollary. Corollary 1. Let S1 . . S|T| be the conversion of each Ci into sample space according to Eq.

Not surprisingly, as the number of samples in the data set is increased, the time to identify components also increases. The first clustering stage in the algorithm must compute a distance matrix between all procedures in the data set, which increases with the number of samples. 10000 Seconds 1000 100 Louvain (w/o splits) Louvain 10 1 TC1 (58 Samples) TC2 (258 Samples) TC3 (527 Samples) Fig. 6. Time to identify components on the IV&V data-set. The results using K-means are not shown as they are nearly identical to the Louvain results.

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