Android Malware Detection Using Parallel Machine Learning Classifiers

Suleiman Y. Yerima, Sakir Sezer, Igor Muttik

Research output: Contribution to conferencePaperpeer-review

63 Citations (Scopus)
875 Downloads (Pure)


Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.
Original languageEnglish
Number of pages6
Publication statusPublished - Sep 2014
Event8th International Conference on Next Generation Mobile Apps, Services and Technologies - Oxford, United Kingdom
Duration: 10 Sep 201412 Sep 2014


Conference8th International Conference on Next Generation Mobile Apps, Services and Technologies
Country/TerritoryUnited Kingdom


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


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