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Towards Privacy Risk Analysis in Android Applications Using Machine Learning Approaches

Author

Listed:
  • Kavita Sharma

    (Department of Computer Engineering, National Institute of Technology, Kurukshetra, India)

  • B. B. Gupta

    (Department of Computer Engineering, National Institute of Technology, Kurukshetra, India)

Abstract

Android-based devices easily fall prey to an attack due to its free availability in the android market. These Android applications are not certified by the legitimate organization. If the user cannot distinguish between the set of permissions requested by an application and its risk, then an attacker can easily exploit the permissions to propagate malware. In this article, the authors present an approach for privacy risk analysis in Android applications using machine learning. The proposed approach can analyse and identify the malware application permissions. Here, the authors achieved high accuracy and improved F-measure through analyzing the proposed method on the M0Droid dataset and completed testing on an extensive test set with malware from the Androzoo dataset and benign applications from the Drebin dataset.

Suggested Citation

  • Kavita Sharma & B. B. Gupta, 2019. "Towards Privacy Risk Analysis in Android Applications Using Machine Learning Approaches," International Journal of E-Services and Mobile Applications (IJESMA), IGI Global, vol. 11(2), pages 1-21, April.
  • Handle: RePEc:igg:jesma0:v:11:y:2019:i:2:p:1-21
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    Cited by:

    1. Sharfah Ratibah Tuan Mat & Mohd Faizal Ab Razak & Mohd Nizam Mohmad Kahar & Juliza Mohamad Arif & Salwana Mohamad & Ahmad Firdaus, 2021. "Towards a systematic description of the field using bibliometric analysis: malware evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2013-2055, March.
    2. Zhou, Yufei & Wang, Sihan & Zhang, Nuo, 2023. "Dynamic decision-making analysis of Netflix's decision to not provide ad-supported subscriptions," Technological Forecasting and Social Change, Elsevier, vol. 187(C).

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