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Recognition method for detecting software vulnerability with weight deviation based on graph neural network

A software vulnerability and neural network technology, applied in the direction of instruments, electrical digital data processing, platform integrity maintenance, etc.

Pending Publication Date: 2021-09-10
DALIAN MARITIME UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, in practical applications, defective samples often account for a minority of all codes

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  • Recognition method for detecting software vulnerability with weight deviation based on graph neural network
  • Recognition method for detecting software vulnerability with weight deviation based on graph neural network
  • Recognition method for detecting software vulnerability with weight deviation based on graph neural network

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Embodiment Construction

[0027] In order to make the technical solutions and advantages of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described below in conjunction with the drawings in the embodiments of the present invention:

[0028] Such as figure 1 A method for identifying software vulnerabilities with weight bias based on a graph neural network is shown, which specifically includes the following steps:

[0029] Step 1: Graph Embedding Model

[0030] The words and symbols in the source code are regarded as a node, and the source code composition graph representation G={V,E} is used as the input of GGNN. V represents a point set, and E represents an edge set. First, you need to obtain the initial eigenvalues ​​of each node, and you also need to obtain the initial characteristics of the connected edges of each graph. For the initial feature of the edge, the graph embedding model uses the NL Graph Embedding meth...

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Abstract

The invention discloses a recognition method for detecting software vulnerability with weight deviation based on a graph neural network. The method comprises the following steps: regarding words and symbols in a source code as a node, representing the source code by adopting a constituent graph, and acquiring an initial characteristic value of each node and an initial characteristic of a connected edge of each graph; taking the generated composition graph as input, finally combining the information output by the reset gate, the information output by the update gate and the information of the own node, and outputting a node activation value through a Sigmoid activation function as a node state at a final moment; after the word nodes are fully updated, aggregating the words to the graphic-level representation of the function codes, and generating a final vulnerability recognition result based on the representation; and minimizing the loss value of the defective software vulnerability by using a-Dice coefficient so as to identify the software vulnerability.

Description

technical field [0001] The invention relates to the technical field of software monitoring, in particular to a method for identifying software vulnerabilities with weight deviations based on a graph neural network. Background technique [0002] Software vulnerability detection is one of the main means to check and discover security vulnerabilities in software systems. Audit the code in the software by using various tools, or analyze the execution process of the software to find software design errors, coding defects, and operational failures. Early vulnerability detection technologies are divided into static analysis methods and dynamic analysis methods according to whether they depend on program operation. With the development of machine learning, researchers have begun to try to predict code vulnerabilities by combining traditional techniques with machine learning. Among them, the static analysis method is generally applied to the development and coding stage of software...

Claims

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Application Information

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IPC IPC(8): G06F21/57
CPCG06F21/577Y02D10/00
Inventor 李辉曲阳刘慧江汪海博赵娇茹刘勇郭世凯陈荣
Owner DALIAN MARITIME UNIVERSITY
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