Survey of Software Vulnerability Mining Methods Based on Machine Learning
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National Key Technologies Research and Development Program, China (2018YFB0803501)

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    Abstract:

    The increasing complexity of software application brings great challenges to software security. Due to the increase of software scale and diversity of vulnerability forms, the high false positives and false negatives of traditional vulnerability mining methods cannot meet the requirements of software security analysis. In recent years, with the rise of artificial intelligence industry, a large number of machine learning methods have been tried to solve the problem of software vulnerability mining. Firstly, the latest research results of applying machine learning method to the research of vulnerability mining are summarized in recent years, and the technical characteristics and workflow are proposed. Then, starting from the core original data features extraction, the existing research is classified according to the code representation form, and the existing research is systematically compared. Finally, based on the summary of the existing research, the challenges in the field of software vulnerability mining based on machine learning are discussed, and the development trends of this field are proposed.

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李韵,黄辰林,王中锋,袁露,王晓川.基于机器学习的软件漏洞挖掘方法综述.软件学报,2020,31(7):2040-2061

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  • Received:November 08,2019
  • Revised:February 07,2020
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  • Online: May 26,2020
  • Published: July 06,2020
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