Vulnerability detection method and system based on self-supervised learning and multi-channel hypergraph neural network

A neural network and vulnerability detection technology, applied in the direction of neural learning methods, biological neural network models, neural architectures, etc., can solve the problems of poor detection of loopholes and insufficient use of high-order relationships in codes, so as to achieve clean data stream information, The effect of low false alarm rate and reduced time overhead

Active Publication Date: 2022-07-08
EAST CHINA NORMAL UNIV
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Problems solved by technology

[0004] In order to solve the problem that the existing loophole detection method cannot fully use the high-order relationship of the code and the loophole dete

Method used

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  • Vulnerability detection method and system based on self-supervised learning and multi-channel hypergraph neural network
  • Vulnerability detection method and system based on self-supervised learning and multi-channel hypergraph neural network
  • Vulnerability detection method and system based on self-supervised learning and multi-channel hypergraph neural network

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Experimental program
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Embodiment

[0103] The specific process of this embodiment is as follows:

[0104] First, select the code dataset QEMU and FFmpeg:

[0105] For the selected code data, the following describes how to convert the code text, such as figure 2 shown:

[0106] (1) Preprocess the code to remove links, special characters, etc. in the code.

[0107] (2) Standardize the code variable names.

[0108] (3) Using the compiler tool Joern to generate abstract syntax tree (AST graph) and control flow graph (CFG graph) of function code.

[0109] (4) Then traverse the token sequence of the leaf nodes in the AST, obtain the data transfer relationship between the code tokens, and generate a data flow graph (DFG graph).

[0110] (5) Then traverse the token sequences in the leaf nodes in the AST, match with the code text, and generate a sequence relationship diagram (SRG diagram) representing the sequence relationship between the code tokens before and after.

[0111] (6) Then, according to the Linear Di...

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Abstract

The invention discloses a vulnerability detection method based on self-supervised learning and multi-channel hypergraph neural network. Motifs representing high-order information are constructed for different channels, and the code sequence graph is sampled according to the motif to obtain a multi-channel code sequence hypergraph. The preprocessed code text data is trained as word vector representation using word2vec. Finally, the code sequence hypergraph and labels are used as training data to train the hypergraph neural network, and the node representation and the hypergraph representation are obtained by learning, and then the hypergraph representation is spliced, and the graph is classified by a single-layer perceptron. The invention simultaneously introduces self-supervised learning to make up for the loss of information between multiple channels, and integrates the mutual information between multiple channels through self-supervised learning, which has better interpretability and vulnerability detection effect. The present invention also provides a system for implementing the above method.

Description

technical field [0001] The invention belongs to the technical field of computer information security, and relates to a vulnerability detection method and system based on self-supervised learning and a multi-channel hypergraph neural network, in particular to a method of constructing function-level codes into hypergraphs, utilizing self-supervised learning and hypergraphs A neural network's method of determining whether code is vulnerable. Background technique [0002] In recent years, with the rapid development of computer software technology, a large amount of software has been developed, and there are hidden loopholes in the software. Incorrect programming habits of developers and insufficient software testing of testers have resulted in a large number of hidden bugs in the code that have not been discovered. Hackers can take advantage of hidden loopholes, destroy systems, steal data, and cause greater harm to enterprises and countries. Therefore, vulnerability detection...

Claims

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Application Information

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IPC IPC(8): G06F21/57G06N3/04G06N3/08
CPCG06F21/577G06N3/04G06N3/08
Inventor 王骏王志远张伟
Owner EAST CHINA NORMAL UNIV
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