Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 508-515.doi: 10.11896/jsjkx.210700103

• Information Security • Previous Articles     Next Articles

Android Malware Detection Method Based on Heterogeneous Model Fusion

YAO Ye, ZHU Yi-an, QIAN Liang, JIA Yao, ZHANG Li-xiang, LIU Rui-liang   

  1. School of Computer Science,Northwestern Polytechnical University,Xi'an 710129,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:YAO Ye,born in 1972,associate professor,master supervisor.His main research interests include software security testing,network system security evaluation,industrial Internet and security technology.
    ZHU Yi-an,born in 1961,professor,doctoral supervisor.His main research interests include parallel computing,network and information security,complex system modeling and analysis,big data intelligent processing technology,security critical operating system.
  • Supported by:
    National key Research and Development Program of China(2020YFB1712200),Key Research Development Plan of Shaanxi Province of China(2019ZDLGY12-07),Xi'an City Science and Technology Plan Project of China(GXYD192.1),Innovation Leading Project of Taicang City of China (TC2019DYDS06) and Dongguan City Science and Technology Equipment Mobilization Project of China(KZ2018-14).

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Abstract: Aiming at the problem of limited detection accuracy of a single classification model,this paper proposes an Android malware detection method based on heterogeneous model fusion.Firstly,by identifying and collecting the mixed feature information of malicious software,the random forest algorithm based on CART decision tree and the Adaboost algorithm based on MLP are used to construct the integrated learning model respectively,and then the two classifiers are fused by Blending algorithm.Finally,a heterogeneous model fusion classifier is obtained.On this basis,the mobile terminal malware detection is implemented.Experimental results show that the proposed method can effectively overcome the problem of insufficient accuracy of single classification model.

Key words: Android system, Machine learning, Malware, Mobile terminal, Model fusion

CLC Number: 

  • TP391.9
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