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A static analysis approach for Android permission-based malware detection systems

Author

Listed:
  • Juliza Mohamad Arif
  • Mohd Faizal Ab Razak
  • Suryanti Awang
  • Sharfah Ratibah Tuan Mat
  • Nor Syahidatul Nadiah Ismail
  • Ahmad Firdaus

Abstract

The evolution of malware is causing mobile devices to crash with increasing frequency. Therefore, adequate security evaluations that detect Android malware are crucial. Two techniques can be used in this regard: Static analysis, which meticulously examines the full codes of applications, and dynamic analysis, which monitors malware behaviour. While both perform security evaluations successfully, there is still room for improvement. The goal of this research is to examine the effectiveness of static analysis to detect Android malware by using permission-based features. This study proposes machine learning with different sets of classifiers was used to evaluate Android malware detection. The feature selection method in this study was applied to determine which features were most capable of distinguishing malware. A total of 5,000 Drebin malware samples and 5,000 Androzoo benign samples were utilised. The performances of the different sets of classifiers were then compared. The results indicated that with a TPR value of 91.6%, the Random Forest algorithm achieved the highest level of accuracy in malware detection.

Suggested Citation

  • Juliza Mohamad Arif & Mohd Faizal Ab Razak & Suryanti Awang & Sharfah Ratibah Tuan Mat & Nor Syahidatul Nadiah Ismail & Ahmad Firdaus, 2021. "A static analysis approach for Android permission-based malware detection systems," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-23, September.
  • Handle: RePEc:plo:pone00:0257968
    DOI: 10.1371/journal.pone.0257968
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    1. repec:igg:jssoe0:v:9:y:2019:i:2:p:12-34 is not listed on IDEAS
    2. Anindya Ghose & Beibei Li & Siyuan Liu, 2019. "Mobile Targeting Using Customer Trajectory Patterns," Management Science, INFORMS, vol. 65(11), pages 5027-5049, November.
    3. Hye Rim Hong & Young In Kim, 2019. "A mobile application for personal colour analysis," Cogent Business & Management, Taylor & Francis Journals, vol. 6(1), pages 1576828-157, January.
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    Cited by:

    1. Jiyun Yang & Zhibo Zhang & Heng Zhang & JiaWen Fan, 2022. "Android malware detection method based on highly distinguishable static features and DenseNet," PLOS ONE, Public Library of Science, vol. 17(11), pages 1-34, November.

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