Malware Phylogenetics Based on the Multiview Graphical Lasso

  • Blake Anderson
  • Terran Lane
  • Curtis Hash
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8819)

Abstract

Malware phylogenetics has gained a lot of traction over the past several years. More recently, researchers have begun looking at directed acyclic graphs (DAG) to model the evolutionary relationships between samples of malware. Phylogenetic graphs offer analysts a better understanding of how malware has evolved by clearly illustrating the lineage of a given family. In this paper, we present a novel algorithm based on graphical lasso. We extend graphical lasso to incorporate multiple views, both static and dynamic, of malware. For each program family, a convex combination of the views is found such that the objective function of graphical lasso is maximized. Learning the weights of each view on a per-family basis, as opposed to treating all views as an extended feature vector, is essential in the malware domain because different families employ different obfuscation strategies which limits the information of different views. We demonstrate results on three malicious families and two benign families where the ground truth is known.

Keywords

Gaussian Graphical Models Malware Multiview Learning 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Blake Anderson
    • 1
  • Terran Lane
    • 2
  • Curtis Hash
    • 1
  1. 1.Los Alamos National LaboratoryUSA
  2. 2.Google, Inc.USA

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