Android Malware Detection: an Eigenspace Analysis Approach

Suleiman Y. Yerima, Sakir Sezer, Igor Muttik

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

23 Citations (Scopus)
336 Downloads (Pure)


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 Inc.
Number of pages7
Publication statusPublished - 03 Sept 2015
Event2015 SAI Conference - London, United Kingdom
Duration: 28 Jul 201530 Sept 2015


Conference2015 SAI Conference
Country/TerritoryUnited Kingdom


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


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