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Android Malware Detection Method Based on Heterogeneous Model Fusion

Ji suan ji ke xue, 2022-01, Vol.49, p.508

Copyright Guojia Kexue Jishu Bu 2022 ;ISSN: 1002-137X ;DOI: 10.11896/jsjkx.210700103

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  • Title:
    Android Malware Detection Method Based on Heterogeneous Model Fusion
  • Author: Yao, Ye ; Zhu, Yi-an ; Qian, Liang ; Jia, Yao ; Zhang, Li-xiang ; Liu, Rui-liang
  • Subjects: Algorithms ; Classification ; Classifiers ; Decision trees ; Machine learning ; Malware
  • Is Part Of: Ji suan ji ke xue, 2022-01, Vol.49, p.508
  • Description: 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.
  • Publisher: Chongqing: Guojia Kexue Jishu Bu
  • Language: Chinese
  • Identifier: ISSN: 1002-137X
    DOI: 10.11896/jsjkx.210700103
  • Source: DOAJ Directory of Open Access Journals
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