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Volume 42 Issue 9
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Article Navigation >  Journal of Electronics & Information Technology  > 2020  >  42(9): 2082-2094
Yi CHEN, Di TANG, Wei ZOU. Android Malware Detection Based on Deep Learning: Achievements and Challenges[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2082-2094. doi: 10.11999/JEIT200009
Citation: Yi CHEN, Di TANG, Wei ZOU. Android Malware Detection Based on Deep Learning: Achievements and Challenges[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2082-2094. doi: 10.11999/JEIT200009
shu

Android Malware Detection Based on Deep Learning: Achievements and Challenges

doi: 10.11999/JEIT200009
  • 1.

    Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China

  • 2.

    Chinese University of Hong Kong, Hongkong 999077, China

  • 3.

    Key Laboratory of Network Assessment Technology, Chinese Academy of Sciences, Beijing 100093, China

  • 4.

    School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100049, China

Funds:  Foundation of Key Laboratory of Network Assessment Technology, Chinese Academy of Sciences (CXJJ-19S022)
  • Received Date: 2020-01-20
  • Rev Recd Date: 2020-07-30
    • Available Online: 2020-08-07
  • Publish Date: 2020-09-27
    Abstract
  • With the prosperous of Android applications, Android malware has been scattered everywhere, which raises the serious security risk to users. On the other hand, the rapid developing of deep learning fires the combat between the two sides of malware detection. Inducing deep learning technologies into Android malware detection becomes the hottest topic of society. This paper summarizes the existing achievements of malware detection from four aspects: Data collection, feature construction, network structure and detection performance. Finally, the current limitations and facing challenges followed by the future researches are discussed.
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