Automated Vulnerability Detection in Source Code Using Quantum Natural Language Processing Article Swipe
Mst Shapna Akter
,
Hossain Shahriar
,
Zakirul Alam Bhuiya
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2303.07525
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2303.07525
One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. These flaws are highly likely ex-ploited and lead to system compromise, data leakage, or denial of ser-vice. C and C++ open source code are now available in order to create a large-scale, classical machine-learning and quantum machine-learning system for function-level vulnerability identification. We assembled a siz-able dataset of millions of open-source functions that point to poten-tial exploits. We created an efficient and scalable vulnerability detection method based on a deep neural network model Long Short Term Memory (LSTM), and quantum machine learning model Long Short Term Memory (QLSTM), that can learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the de-pendency. Therefore, We keep the semantic and syntactic information using state of the art word embedding algorithms such as Glove and fastText. The embedded vectors are subsequently fed into the classical and quantum convolutional neural networks to classify the possible vulnerabilities. To measure the performance, we used evaluation metrics such as F1 score, precision, re-call, accuracy, and total execution time. We made a comparison between the results derived from the classical LSTM and quantum LSTM using basic feature representation as well as semantic and syntactic represen-tation. We found that the QLSTM with semantic and syntactic features detects significantly accurate vulnerability and runs faster than its classical counterpart.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2303.07525
- https://arxiv.org/pdf/2303.07525
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4327486619Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2303.07525Digital Object Identifier
- Title
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Automated Vulnerability Detection in Source Code Using Quantum Natural Language ProcessingWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-03-13Full publication date if available
- Authors
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Mst Shapna Akter, Hossain Shahriar, Zakirul Alam BhuiyaList of authors in order
- Landing page
-
https://arxiv.org/abs/2303.07525Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2303.07525Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2303.07525Direct OA link when available
- Concepts
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Computer science, Source code, Artificial intelligence, Scalability, Convolutional neural network, Natural language processing, Deep learning, Programming language, DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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