Fine-grained vulnerability detection method based on depth features

A vulnerability detection and deep feature technology, applied in the field of cyberspace security, can solve the problems of tediousness, inability to guarantee the false positive rate and false negative rate of vulnerability detection results, and achieve the effect of improving the accuracy

Active Publication Date: 2019-04-19
HUAZHONG UNIV OF SCI & TECH
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Problems solved by technology

[0003] In view of the above defects or improvement needs of the prior art, the purpose of the present invention is to solve the problem that the existing manually defined det...

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  • Fine-grained vulnerability detection method based on depth features
  • Fine-grained vulnerability detection method based on depth features
  • Fine-grained vulnerability detection method based on depth features

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Embodiment Construction

[0056] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and descriptions thereof. It should be understood that the specific steps described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various steps of the present invention described below can be combined with each other as long as they do not constitute conflicts with each other.

[0057] Deep learning-based vulnerability detection schemes reduce the tedious work of human experts, because they do not need to define the schema of a specific vulnerability, but only the unit and format of detected vulnerabilities. Compared with traditional machine learning technology, deep learning technology can further reduce the dependence on human experts. The most advanced deep learning-base...

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Abstract

The invention discloses a fine-grained vulnerability detection method based on depth characteristics. The fine-grained vulnerability detection method comprises the following two stages: a training stage and a detection stage. The training stage comprises the steps that a large number of programs with vulnerabilities and without vulnerabilities are collected; Preprocessing the programs, and extracting program slices from the program dependency graph; Labeling the generated program slice according to the vulnerability type; Extracting a program focus point from the program slice according to a security analysis rule; Converting the program slice and the program focus point into vectors; Building a vulnerability detection model based on deep learning, and using vectors to train model parameters to be optimal; And finally, a trained vulnerability detection model based on deep learning is obtained. The detection stage comprises the following steps: extracting a program slice and a program focus point from a program to be detected according to a source code processing mode of the training stage, and respectively converting the program slice and the program focus point into vectors; And classifying the vectors by using the trained vulnerability detection model, and finally generating a vulnerability detection report according to a classification result.

Description

technical field [0001] The invention belongs to the field of cyberspace security, and more specifically relates to a fine-grained vulnerability detection method based on deep features. Background technique [0002] Most network attacks are caused by some software vulnerabilities, although many security technologies have emerged to avoid software vulnerabilities. But due to many reasons, for example, the complexity of software makes software vulnerabilities inevitable. Since software vulnerabilities cannot be avoided, another defense is to detect and patch them as soon as possible. Detecting vulnerabilities in software source code is an indispensable method to solve the problem of software vulnerabilities. Under this approach, many solutions use manually defined vulnerability patterns to detect vulnerabilities. However, manually defining vulnerability patterns is a subjective and tedious task, which usually cannot guarantee the false positive and false negative rates of vu...

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Application Information

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IPC IPC(8): G06F21/57G06F17/27G06N3/04G06F16/35
CPCG06F21/577G06F40/211G06N3/045
Inventor 邹德清王苏娟金海李珍
Owner HUAZHONG UNIV OF SCI & TECH
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