A CNN-based automatic vulnerability detection
DC Field | Value | Language |
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dc.contributor.author | An, Jung Hyun | - |
dc.contributor.author | Wang, Zhan | - |
dc.contributor.author | Joe, Inwhee | - |
dc.date.accessioned | 2023-07-05T04:13:39Z | - |
dc.date.available | 2023-07-05T04:13:39Z | - |
dc.date.created | 2023-06-05 | - |
dc.date.issued | 2023-05 | - |
dc.identifier.issn | 1687-1472 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/186337 | - |
dc.description.abstract | With the advent of the Internet, the activities of individuals and businesses have expanded into the online realm. As a result, vulnerabilities that result in actual breaches can lead to data loss and program failure. The number of breaches is increasing every year, as is the number of vulnerabilities. To address this problem, current research focuses on the detection of vulnerabilities using static analysis techniques. To prevent the propagation of vulnerabilities, a new paradigm is needed to quickly detect vulnerabilities, analyze them, and take actions such as blocking or removing them. Recently, artificial intelligence algorithms such as deep learning have been introduced for vulnerability detection. In this paper, we propose a vulnerability detection model, V-CNN, which aims to detect CWE/CVE (Common Weakness Enumeration/Common Vulnerabilities and Exposures) using CNN (convolutional neural network). We trained CWE for deep learning and redefined vulnerabilities based on CWE. We propose an experimental algorithm to improve vulnerability detection. The accuracy of the proposed V-CNN model is 98%, which exceeds the 95% of the random forest model. Therefore, our V-CNN has excellent correctness detection performance in the field of vulnerability detection. The V-CNN vulnerability detection algorithm can be used instead of static analysis to detect various security vulnerabilities. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Springer Science and Business Media Deutschland GmbH | - |
dc.title | A CNN-based automatic vulnerability detection | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Joe, Inwhee | - |
dc.identifier.doi | 10.1186/s13638-023-02255-2 | - |
dc.identifier.scopusid | 2-s2.0-85160055109 | - |
dc.identifier.wosid | 000992882900001 | - |
dc.identifier.bibliographicCitation | Eurasip Journal on Wireless Communications and Networking, v.2023, no.1, pp.1 - 13 | - |
dc.relation.isPartOf | Eurasip Journal on Wireless Communications and Networking | - |
dc.citation.title | Eurasip Journal on Wireless Communications and Networking | - |
dc.citation.volume | 2023 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 13 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | Backpropagation | - |
dc.subject.keywordPlus | Convolution | - |
dc.subject.keywordPlus | Convolutional neural networks | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Network security | - |
dc.subject.keywordPlus | Common vulnerabilities and exposures | - |
dc.subject.keywordPlus | Common vulnerability and exposure | - |
dc.subject.keywordPlus | Common weakness enumeration | - |
dc.subject.keywordPlus | Convolutional neural network | - |
dc.subject.keywordPlus | Data program | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Network-based | - |
dc.subject.keywordPlus | Security | - |
dc.subject.keywordPlus | Vulnerability | - |
dc.subject.keywordPlus | Vulnerability detection | - |
dc.subject.keywordPlus | Static analysis | - |
dc.subject.keywordAuthor | Convolutional neural networks | - |
dc.subject.keywordAuthor | CVE (common vulnerabilities and exposures) | - |
dc.subject.keywordAuthor | CWE (common weakness enumeration) | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Security | - |
dc.subject.keywordAuthor | Vulnerabilities | - |
dc.identifier.url | https://jwcn-eurasipjournals.springeropen.com/articles/10.1186/s13638-023-02255-2 | - |
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