High accuracy android malware detection using ensemble learning

Suleiman Y. Yerima, Sakir Sezer, Igor Muttik

Research output: Contribution to journalArticlepeer-review

106 Citations (Scopus)
504 Downloads (Pure)


With over 50 billion downloads and more than 1.3 million apps in Google’s official market, Android has continued to gain popularity amongst smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus this paper proposes an approach that utilizes ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3 % to 99% detection accuracy with very low false positive rates.
Original languageEnglish
Number of pages8
JournalIET Information Security
Issue number6
Early online date29 Apr 2015
Publication statusPublished - Nov 2015


  • malware detection
  • mobile security
  • ensemble learning
  • machine learning
  • data mining
  • android
  • static analysis
  • random forest


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