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Android Malware Clustering Through Malicious Payload Mining

  • Yuping LiEmail author
  • Jiyong Jang
  • Xin Hu
  • Xinming Ou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10453)

Abstract

Clustering has been well studied for desktop malware analysis as an effective triage method. Conventional similarity-based clustering techniques, however, cannot be immediately applied to Android malware analysis due to the excessive use of third-party libraries in Android application development and the widespread use of repackaging in malware development. We design and implement an Android malware clustering system through iterative mining of malicious payload and checking whether malware samples share the same version of malicious payload. Our system utilizes a hierarchical clustering technique and an efficient bit-vector format to represent Android apps. Experimental results demonstrate that our clustering approach achieves precision of 0.90 and recall of 0.75 for Android Genome malware dataset, and average precision of 0.98 and recall of 0.96 with respect to manually verified ground-truth.

Notes

Acknowledgment

This work was partially supported by the U.S. National Science Foundation under Grant No. 1314925, 1622402 and 1717862. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Supplementary material

440190_1_En_9_MOESM1_ESM.txt (1 kb)
Supplementary material 1 (txt 1 KB)

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of South FloridaTampaUSA
  2. 2.IBM ResearchYorktown HeightsUSA
  3. 3.PinterestSan FranciscoUSA

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