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; Required Readings

@ARTICLE{ Tamersoy2014,
  title        = {{Guilt by Association: Large Scale Malware Detection by Mining File-relation Graphs}},
  author       = {Tamersoy, Acar and Roundy, Kevin and Chau, Duen Horng},
  journal      = {Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '14},
  keywords     = {against novel threats,belief propa-,computer security providers recognize,file graph,graph mining,malware detection,spond with better protection,the goal,the need to re-},
  year         = {2014},
  url          = {http://dl.acm.org/citation.cfm?doid=2623330.2623342},
  isbn         = {9781450329569},
  abstract     = {The increasing sophistication of malicious software calls for new defensive techniques that are harder to evade, and are capable of protecting users against novel threats. We present Aesop, a scalable algorithm that identifies malicious exe- cutable files by applying Aesop'smoral that“aman is known by the company he keeps.” We use a large dataset volun- tarily contributed by the members of Norton Community Watch, consisting of partial lists of the files that exist on their machines, to identify close relationships between files that often appear together on machines. Aesop leverages locality-sensitive hashing to measure the strength of these inter-file relationships to construct a graph, on which it per- forms large scale inference by propagating information from the labeled files (as benign or malicious) to the preponder- ance of unlabeled files. Aesop attained early labeling of 99{\%} of benign files and 79{\%} of malicious files, over a week before they are labeled by the state-of-the-art techniques, with a 0.9961 true positive rate at flagging malware, at 0.0001 false positive rate.},
  pages        = {1524--1533},
  doi          = {10.1145/2623330.2623342},

  studentfirstname ={},
  studentlastname ={},
  summary =	 {},
  contribution1 ={},
  contribution2 ={},
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  contribution5 ={},
  weakness1 =	 {},
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  weakness3 =	 {},
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  weakness5 =	 {},
  interesting =	 {high/med/low},
  opinions =	 {},
}


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