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Hybrid malware detection approach with feedback-directed machine learning

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Acknowledgements

This work was supported in part by National Key Research and Development Program of China (Grant No. 2018YFB1003702), National Natural Science Foundation of China (Grant No. 61872274), and Hunan Provincial Natural Science Foundation of China for Distinguished Young Scholars (Grant No. 2018JJ1025).

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Authors and Affiliations

  1. Hunan Provincial Key Lab of Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, China

    Zhetao Li & Wenlin Li

  2. The College of Information Engineering, Xiangtan University, Xiangtan, 411105, China

    Zhetao Li & Wenlin Li

  3. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Fuyuan Lin & Yi Sun

  4. University of Chinese Academy of Sciences, Beijing, 100049, China

    Fuyuan Lin & Yi Sun

  5. School of Computer Science, Fudan University, Shanghai, 200433, China

    Min Yang & Yuan Zhang

  6. Shanghai Institute of Intelligent Electronics & Systems, Shanghai, 200433, China

    Min Yang

  7. Shanghai Institute for Advanced Communication and Data Science, Shanghai, 200433, China

    Min Yang

  8. Shanghai Key Laboratory of Data Science, Fudan University, Shanghai, 200433, China

    Min Yang & Yuan Zhang

  9. School of Cyber Science and Engineering, Wuhan University, Wuhan, 430072, China

    Zhibo Wang

Authors
  1. Zhetao Li
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  2. Wenlin Li
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  3. Fuyuan Lin
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  4. Yi Sun
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  5. Min Yang
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  6. Yuan Zhang
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  7. Zhibo Wang
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Corresponding author

Correspondence to Zhibo Wang.

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Cite this article

Li, Z., Li, W., Lin, F. et al. Hybrid malware detection approach with feedback-directed machine learning. Sci. China Inf. Sci. 63, 139103 (2020). https://doi.org/10.1007/s11432-018-9615-8

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