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Deep Learning-Based SBOM Defect Detection for Medical Devices
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Publication Year
2024-01-01
Journal
6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024, pp.47-51
Keyword
Artificial Intelligence (AI)CybersecurityDeep LearningMedical DeviceSBOMSoft-ware Defect Detection
Mesh Keyword
Artificial intelligenceBill of materialsCyber securityDeep learningDefect detectionHealthcare servicesMaterial defect detectionMedical DevicesSoft-ware defect detectionSoftware bill of material
All Science Classification Codes (ASJC)
Artificial IntelligenceComputer Networks and CommunicationsComputer Science ApplicationsComputer Vision and Pattern RecognitionInformation SystemsSafety, Risk, Reliability and QualityHealth Informatics
Abstract
This paper focuses on the digital innovation brought about by the advancement of ICT technology in the healthcare field. In particular, the introduction of cutting-edge technologies such as AI are contributing to the improvement of health care services, but at the same time, the risk of cybersecurity threats and software security problems in medical devices are emerging as well. Therefore, the importance of SBOM (Software Bill of Materials) to respond to such problems has now been emphasized, and this paper focuses on strengthening the cybersecurity of medical devices through the creation and application of SBOM. This study develops an AI-based software defect detection method and seeks to ensure the reliability and safety of medical devices by proposing an efficient method compared to existing rule-based approaches. This study aims to contribute to strengthening security by detecting flaws in medical devices through AI and establishing a technical foundation for providing ultimate quality healthcare services.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/37125
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85189940927&origin=inward
DOI
https://doi.org/10.1109/icaiic60209.2024.10463483
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10463165
Type
Conference
Funding
This research was supported in part by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2024-2021-0-01835)
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Kim, Ki-Hyung  Image
Kim, Ki-Hyung 김기형
Department of Cyber Security
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