Source node loophole detection method based on integrated neural network

A vulnerability detection and neural network technology, applied in the field of detection, can solve problems such as complex software security vulnerability detection requirements

Inactive Publication Date: 2015-07-29
CHINA ELECTRIC POWER RES INST +3
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The detection of software security vulnerabilities requires more complex requirements than earlier

Method used

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  • Source node loophole detection method based on integrated neural network
  • Source node loophole detection method based on integrated neural network
  • Source node loophole detection method based on integrated neural network

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Embodiment Construction

[0053] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0054] Such as figure 1 , the present invention provides a kind of source code loophole detection method based on integrated neural network, described method comprises the following steps:

[0055] Step 1: Establish a vulnerability detection model;

[0056] Step 2: Perform source code vulnerability detection based on the vulnerability detection model.

[0057] Described step 1 comprises the following steps:

[0058] Step 1-1: given training set;

[0059] Step 1-2: Preprocess the source code, and then perform feature extraction based on the N-gram algorithm;

[0060] Step 1-3: Based on the extracted features, use the ReliefF algorithm for feature selection;

[0061] Steps 1-4: Source code vulnerability detection learning based on the integrated neural network.

[0062] In the step 1-1, given the training set D={d through manual classification 1 , d 2 ,...

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Abstract

The invention provides a source node loophole detection method based on an integrated neural network. Source nodes are processed with an N-Gram algorithm, and a represented by an N-Gram set; implicit characteristics are mined from the N-Gram set with a probability statistics method, so that the attribute of code content is ensured, and the sequence correlation property among the codes is kept; characteristic selection is performed with a ReliefF algorithm to calculate a characteristic weight; specific to the aim of solving extreme imbalance of sample data, the functions of small type samples need to be fully considered during calculation, and different neighbor values are set for different types so that the characteristics of the small sample data can play certain roles in calculation; a multilayer feed-forward network is trained with a BP algorithm in the neural network for serving as individual networks, the trust scope of each individual network is learned through a series of parameter learning of identification rate, reject rate and the like with a DS evidence theory, and a final detection result is summarized according to different trust values of each network, so that accurate and effective source node loophole detection is realized.

Description

technical field [0001] The invention relates to a detection method, in particular to a source code vulnerability detection method based on an integrated neural network. Background technique [0002] With the increasing number of hacker attacks and the proliferation of worms on the Internet, information security has gradually become the focus of people's eyes. A core problem in information security is the software security loopholes in computer systems. Malicious attackers can use these security loopholes to elevate their privileges, access unauthorized resources, and even destroy sensitive data. [0003] The heavy losses caused by software security loopholes and the increasing number of loopholes have made people realize the importance of software security. Since the 1990s, information security scholars and computer security researchers have begun to study computer security vulnerabilities, the most well-known of which is the buffer overflow attack vulnerability. However, ...

Claims

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Application Information

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IPC IPC(8): G06F11/36G06N3/02
Inventor 周诚张涛马媛媛楚杰汪晨时坚李伟伟张波黄秀丽费稼轩
Owner CHINA ELECTRIC POWER RES INST
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