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Optimal Machine Learning Model for Detecting Normal and Malicious Android Apps

  • Journal of Internet of Things and Convergence
  • Abbr : JKIOTS
  • 2020, 6(2), pp.1-10
  • Publisher : The Korea Internet of Things Society
  • Research Area : Engineering > Computer Science > Internet Information Processing
  • Received : April 20, 2020
  • Accepted : June 23, 2020
  • Published : June 30, 2020

Lee, Hyung Woo 1 HanSeong Lee 1

1한신대학교

Candidate

ABSTRACT

The mobile application based on the Android platform is simple to decompile, making it possible to create malicious applications similar to normal ones, and can easily distribute the created malicious apps through the Android third party app store. In this case, the Android malicious application in the smartphone causes several problems such as leakage of personal information in the device, transmission of premium SMS, and leakage of location information and call records. Therefore, it is necessary to select a optimal model that provides the best performance among the machine learning techniques that have published recently, and provide a technique to automatically identify malicious Android apps. Therefore, in this paper, after adopting the feature engineering to Android apps on official test set, a total of four performance evaluation experiments were conducted to select the machine learning model that provides the optimal performance for Android malicious app detection.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.