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A new method of software vulnerability detection based on a quantum neural network

In the field of network security, although there has been related work on software vulnerability detection based on classic machine learning, detection ability is directly proportional to the scale of training data. A quantum neural network has been proven to solve the memory bottleneck problem of c...

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Autores principales: Zhou, Xin, Pang, Jianmin, Yue, Feng, Liu, Fudong, Guo, Jiayu, Liu, Wenfu, Song, Zhihui, Shu, Guoqiang, Xia, Bing, Shan, Zheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9110356/
https://www.ncbi.nlm.nih.gov/pubmed/35577855
http://dx.doi.org/10.1038/s41598-022-11227-3
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author Zhou, Xin
Pang, Jianmin
Yue, Feng
Liu, Fudong
Guo, Jiayu
Liu, Wenfu
Song, Zhihui
Shu, Guoqiang
Xia, Bing
Shan, Zheng
author_facet Zhou, Xin
Pang, Jianmin
Yue, Feng
Liu, Fudong
Guo, Jiayu
Liu, Wenfu
Song, Zhihui
Shu, Guoqiang
Xia, Bing
Shan, Zheng
author_sort Zhou, Xin
collection PubMed
description In the field of network security, although there has been related work on software vulnerability detection based on classic machine learning, detection ability is directly proportional to the scale of training data. A quantum neural network has been proven to solve the memory bottleneck problem of classical machine learning, so it has far-reaching prospects in the field of vulnerability detection. To fill the gap in this field, we propose a quantum neural network structure named QDENN for software vulnerability detection. This work is the first attempt to implement word embedding of vulnerability codes based on a quantum neural network, which proves the feasibility of a quantum neural network in the field of vulnerability detection. Experiments demonstrate that our proposed QDENN can effectively solve the inconsistent input length problem of quantum neural networks and the problem of batch processing with long sentences. Furthermore, it can give full play to the advantages of quantum computing and realize a vulnerability detection model at the cost of a small amount of measurement. Compared to other quantum neural networks, our proposed QDENN can achieve higher vulnerability detection accuracy. On the sub dataset with a small-scale interval, the model accuracy rate reaches 99%. On each subinterval data, the best average vulnerability detection accuracy of the model reaches 86.3%.
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spelling pubmed-91103562022-05-18 A new method of software vulnerability detection based on a quantum neural network Zhou, Xin Pang, Jianmin Yue, Feng Liu, Fudong Guo, Jiayu Liu, Wenfu Song, Zhihui Shu, Guoqiang Xia, Bing Shan, Zheng Sci Rep Article In the field of network security, although there has been related work on software vulnerability detection based on classic machine learning, detection ability is directly proportional to the scale of training data. A quantum neural network has been proven to solve the memory bottleneck problem of classical machine learning, so it has far-reaching prospects in the field of vulnerability detection. To fill the gap in this field, we propose a quantum neural network structure named QDENN for software vulnerability detection. This work is the first attempt to implement word embedding of vulnerability codes based on a quantum neural network, which proves the feasibility of a quantum neural network in the field of vulnerability detection. Experiments demonstrate that our proposed QDENN can effectively solve the inconsistent input length problem of quantum neural networks and the problem of batch processing with long sentences. Furthermore, it can give full play to the advantages of quantum computing and realize a vulnerability detection model at the cost of a small amount of measurement. Compared to other quantum neural networks, our proposed QDENN can achieve higher vulnerability detection accuracy. On the sub dataset with a small-scale interval, the model accuracy rate reaches 99%. On each subinterval data, the best average vulnerability detection accuracy of the model reaches 86.3%. Nature Publishing Group UK 2022-05-16 /pmc/articles/PMC9110356/ /pubmed/35577855 http://dx.doi.org/10.1038/s41598-022-11227-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Zhou, Xin
Pang, Jianmin
Yue, Feng
Liu, Fudong
Guo, Jiayu
Liu, Wenfu
Song, Zhihui
Shu, Guoqiang
Xia, Bing
Shan, Zheng
A new method of software vulnerability detection based on a quantum neural network
title A new method of software vulnerability detection based on a quantum neural network
title_full A new method of software vulnerability detection based on a quantum neural network
title_fullStr A new method of software vulnerability detection based on a quantum neural network
title_full_unstemmed A new method of software vulnerability detection based on a quantum neural network
title_short A new method of software vulnerability detection based on a quantum neural network
title_sort new method of software vulnerability detection based on a quantum neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9110356/
https://www.ncbi.nlm.nih.gov/pubmed/35577855
http://dx.doi.org/10.1038/s41598-022-11227-3
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