Advanced Security Strategies for Software Vulnerability Protection: A Multi-Scale Detection Model With FPGA-RFID.
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| Title: | Advanced Security Strategies for Software Vulnerability Protection: A Multi-Scale Detection Model With FPGA-RFID. |
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| Authors: | Wang, Yunze1 (AUTHOR), Xing, Dan1 (AUTHOR) paper0804@163.com, Xu, Man1 (AUTHOR) |
| Source: | International Journal of RF Technologies: Research & Applications. Aug2025, Vol. 15 Issue 2, p107-124. 18p. |
| Subjects: | Computer security vulnerabilities, Gate array circuits, Computer software security, Feature extraction, Extraction techniques |
| Abstract: | This paper proposes a multi-scale software vulnerability detection model using Radio Frequency Identification (RFID) target recognition technology and Field-Programmable Gate Array (FPGA) hardware design. The systematic analysis of RFID systems and FPGA architectures provides the foundation for constructing robust security solutions against code-based threats. The Scalable Vulnerability Detection Method (SVDM) is developed leveraging multi-scale code metrics and deep feature extraction techniques. The evaluations of vulnerability datasets CWE-119 and CWE-399 demonstrated over 84% accuracy, recall, and F1 score. The precision, recall, and F1 score of the CWE-399 vulnerability dataset in the SVDM were 85.15%, 85.12%, and 84.79%, respectively, while the values of the CWE-199 dataset were 83.12%, 82.16%, and 82.38%, respectively. Compared with existing methods, the functionality of SVDM was further highlighted. This research provides an extensible and adaptable framework for identifying security vulnerabilities with both hardware and software techniques. It can optimize threat detection, risk analysis, and system integrity across software development and operation life cycles, providing technical reference and scientific support for the security protection of software vulnerabilities, while promoting the development of industries such as intelligent monitoring and traceability. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of RF Technologies: Research & Applications is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Abstract: | This paper proposes a multi-scale software vulnerability detection model using Radio Frequency Identification (RFID) target recognition technology and Field-Programmable Gate Array (FPGA) hardware design. The systematic analysis of RFID systems and FPGA architectures provides the foundation for constructing robust security solutions against code-based threats. The Scalable Vulnerability Detection Method (SVDM) is developed leveraging multi-scale code metrics and deep feature extraction techniques. The evaluations of vulnerability datasets CWE-119 and CWE-399 demonstrated over 84% accuracy, recall, and F1 score. The precision, recall, and F1 score of the CWE-399 vulnerability dataset in the SVDM were 85.15%, 85.12%, and 84.79%, respectively, while the values of the CWE-199 dataset were 83.12%, 82.16%, and 82.38%, respectively. Compared with existing methods, the functionality of SVDM was further highlighted. This research provides an extensible and adaptable framework for identifying security vulnerabilities with both hardware and software techniques. It can optimize threat detection, risk analysis, and system integrity across software development and operation life cycles, providing technical reference and scientific support for the security protection of software vulnerabilities, while promoting the development of industries such as intelligent monitoring and traceability. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 17545730 |
| DOI: | 10.1177/17545730241297786 |
