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Text deep learning-based software security vulnerability prediction method

A deep learning and software security technology, applied in computer security devices, special data processing applications, instruments, etc., can solve the problems of insufficient vulnerability prediction effect and poor feature learning effect, and achieve high accuracy, false alarm rate and missed alarm rate. The effect of low and improved performance indicators

Pending Publication Date: 2018-09-18
BEIJING INSTITUTE OF TECHNOLOGYGY +1
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Problems solved by technology

In this case, the machine learning algorithm based on shallow learning, which is often used at present, has poor feature learning effect compared with deep learning, which directly leads to insufficient vulnerability prediction effect.

Method used

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  • Text deep learning-based software security vulnerability prediction method
  • Text deep learning-based software security vulnerability prediction method
  • Text deep learning-based software security vulnerability prediction method

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Embodiment Construction

[0031] The present invention will be further described below in conjunction with the accompanying drawings.

[0032] Such as figure 2 As shown, the software security vulnerability prediction method based on text deep learning of the present invention comprises the following steps:

[0033] Step 1, software source code text word feature extraction

[0034] For the source code of a software project (that is, the prediction object), take the software module in it as the processing unit, first remove the punctuation marks and code comments that appear in the module source code text, and use spaces as separators for the remaining text Extract each word, and count the number of occurrences of each word in the entire text, and finally normalize the number of occurrences. The result of the processing is the frequency of occurrence of the word, thus obtaining the source code text for this module. The eigenvectors, set to the following representation:

[0035] ComponentName: (Item_1...

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Abstract

The invention discloses a text deep learning-based software security vulnerability prediction method, which learns features and knowledge from a historical software source code text by adopting a deepneural network model and a shallow machine learning algorithm, and can be used for predicting security vulnerabilities in new software source codes. According to the method, structural features in the features of the software source code text are learnt by adopting the deep neural network model; by taking the learnt features as inputs of a classifier, the classifier is subjected to training adjustment; an optimal vulnerability prediction model is obtained; and the method is used for vulnerability prediction of new software modules of software.

Description

technical field [0001] The invention relates to a software security loophole prediction method based on text deep learning, and belongs to the technical field of software security loophole prediction. Background technique [0002] Software security vulnerability prediction can know in advance the possibility or quantity of software vulnerabilities in software source code modules. According to the prediction results, software developers can invest limited time and funds in targeted projects with high probability and large number of vulnerabilities. In the testing of software modules, the efficiency of software testing can be improved. [0003] At present, commonly used software vulnerability prediction uses shallow machine learning methods to establish software vulnerability prediction models. The establishment process is as follows: figure 1 Shown: [0004] ① Establishment of metrics for software source code modules [0005] At present, there are mainly two methods for es...

Claims

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Application Information

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IPC IPC(8): G06F21/57G06F17/30G06F17/27
CPCG06F21/577G06F2221/033G06F40/284
Inventor 危胜军钟浩单纯胡昌振牛中盈
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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