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Automatic Malware Detection Using Deep Learning Based on Static Analysis

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Data Science (ICPCSEE 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 727))

Abstract

Malware detection is an important challenge in the field of information security. The paper proposes a novel method using deep learning based on static analysis. Deep learning has stronger nonlinear expression ability than shallow learning, so it has received much attention from scholar and manufacturers. We use static analysis to extract the malware features are mapped into the input of deep learning. The experiments show that the method is suitable for detecting malware.

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Acknowledgment

The author is grateful to Baosheng Wang and Bo Yu for the guidance and advice, and thanks to the support of the project. The work was supported by Science Foundation of China under (NSFC) (Nos. 61472437, 61303264 and 61379148) and National Basic Research and Development Program of China (973 Program, No. 2012CB315906).

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Authors and Affiliations

  1. School of Computer, National University of Defense Technology, Changsha, 410073, China

    Liu Liu & Baosheng Wang

Authors
  1. Liu Liu
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  2. Baosheng Wang
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Corresponding author

Correspondence to Liu Liu .

Editor information

Editors and Affiliations

  1. Central South University, Changsha, China

    Beiji Zou

  2. Central South University, Changsha, China

    Min Li

  3. Harbin Institute of Technology, Harbin, China

    Hongzhi Wang

  4. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  5. Harbin University of Science and Technology, Harbin, China

    Wei Xie

  6. Harbin Sea of Clouds and Computer Technology, Harbin, China

    Zeguang Lu

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Liu, L., Wang, B. (2017). Automatic Malware Detection Using Deep Learning Based on Static Analysis. In: Zou, B., Li, M., Wang, H., Song, X., Xie, W., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 727. Springer, Singapore. https://doi.org/10.1007/978-981-10-6385-5_42

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  • DOI: https://doi.org/10.1007/978-981-10-6385-5_42

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6384-8

  • Online ISBN: 978-981-10-6385-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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