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Optimal Machine Learning Model for Detecting Normal and Malicious Android Apps

안드로이드 정상 및 악성 앱 판별을 위한 최적합 머신러닝 기법

  • Lee, Hyung-Woo (Div. of Computer Engineering, Hanshin University) ;
  • Lee, HanSeong (Dept. of Computer Engineering, Hanshin University)
  • 이형우 (한신대학교 컴퓨터공학부) ;
  • 이한성 (한신대학교 컴퓨터공학과 대학원)
  • Received : 2020.04.20
  • Accepted : 2020.06.23
  • Published : 2020.06.30

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.

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