Android Malware Detection: an Eigenspace Analysis Approach

Suleiman Y. Yerima, Sakir Sezer, Igor Muttik

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)
184 Downloads (Pure)

Abstract

The battle to mitigate Android malware has become more critical with the emergence of new strains incorporating increasingly sophisticated evasion techniques, in turn necessitating more advanced detection capabilities. Hence, in this paper we propose and evaluate a machine learning based approach based on eigenspace analysis for Android malware detection using features derived from static analysis characterization of Android applications. Empirical evaluation with a dataset of real malware and benign samples show that detection rate of over 96% with a very low false positive rate is achievable using the proposed method.
Original languageEnglish
Title of host publicationProceedings of the 2015 Science and Information Conference
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1236-1242
Number of pages7
DOIs
Publication statusPublished - 03 Sep 2015
Event2015 SAI Conference - London, United Kingdom
Duration: 28 Jul 201530 Sep 2015

Conference

Conference2015 SAI Conference
CountryUnited Kingdom
CityLondon
Period28/07/201530/09/2015

Keywords

  • malware detection
  • machine learning
  • data mining
  • eigenvectors
  • eigenvalue analysis
  • mobile security
  • Android
  • eigenspace
  • static analysis

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  • Cite this

    Yerima, S. Y., Sezer, S., & Muttik, I. (2015). Android Malware Detection: an Eigenspace Analysis Approach. In Proceedings of the 2015 Science and Information Conference (pp. 1236-1242). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/SAI.2015.7237302