Fuzzy test case generation method based on machine learning method

A fuzz testing and machine learning technology, applied in the field of information security, can solve the problems of fuzz testing technology to be improved, security loopholes powerless, uncontrollable cost, etc., to achieve the effect of improving generation efficiency, improving effectiveness, and reducing redundancy

Active Publication Date: 2019-11-12
BEIJING INST OF COMP TECH & APPL
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Problems solved by technology

The current mainstream fuzz testing technology in the test case generation stage has serious use case redundancy, high cost, and problems such as uncontrollable cost.
[0006] (3) In terms of type coverage of security vulner...

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  • Fuzzy test case generation method based on machine learning method
  • Fuzzy test case generation method based on machine learning method
  • Fuzzy test case generation method based on machine learning method

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Embodiment Construction

[0032] In order to make the purpose, content, and advantages of the present invention clearer, the specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0033] Aiming at the three typical problems existing in the current mainstream fuzzing technology, the present invention optimizes the design of the redundancy problem of test cases existing in the current mainstream fuzzing technology. Previously, the taint variable and problem function in the tag recognition program object, combined with the existing seed case generation and screening technologies, can improve the effectiveness of fuzz test cases and reduce the redundancy of fuzz test case sets.

[0034] Test case generation is the core link of fuzz testing method, and the validity of test cases directly affects the accuracy of fuzz test results. Because the traditional fuzz testing technology randomly selects values...

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Abstract

The invention relates to a fuzzy test case generation method based on a machine learning method, and relates to the field of information security. According to the method, the test case redundancy problem existing in the current fuzzy test technology is optimally designed. In the aspect of generating the fuzzy test case oriented to the source program file, before the fuzzy test case is generated,the stain variable and the problem function in the program object are marked and identified, and the existing seed case generation and screening technology is combined, so that the effectiveness of the fuzzy test case can be improved, and the redundancy of a fuzzy test case set is reduced. In the test case generation link, combining machine learning, the feasibility of machine learning for test case simplification is analyzed, to obtain a test case of machine learning and generate an optimization technique idea. According to the method, a machine learning model and an algorithm are adopted toimprove a test case generation link in a fuzzy test process, improve the generation efficiency of the test case, realize redundancy removal of test case combination and achieve the purpose of improving the intelligent degree of the fuzzy test process.

Description

technical field [0001] The invention relates to the technical field of information security, in particular to a method for generating fuzzy test cases based on a machine learning method. Background technique [0002] According to the statistics of security vulnerabilities released by the National Information Security Vulnerability Database (CNNVD), the number of security vulnerabilities announced in my country in 2018 was 23,029, compared with the total number of security vulnerabilities in 2017, which was 18,586, an annual growth rate of about 23.9%. Compared with the surge in the number of security vulnerabilities in 2017, the growth rate of the number of security vulnerabilities in 2018 slowed down. However, this does not mean that the prevention of security vulnerabilities has achieved gratifying results, because the main reason for this phenomenon is that the statistics and publication of security vulnerabilities are more decentralized than before, and a large number of...

Claims

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Application Information

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IPC IPC(8): G06F21/57
CPCG06F21/577G06F2221/033
Inventor 赵磊贾琼常承伟刘滋润杨枭张宏星
Owner BEIJING INST OF COMP TECH & APPL
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