Deep Learning-Based SBOM Defect Detection for Medical Devices - Kim, Heeyeon; Kim, Ki Hyung (6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024, 2024)
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.
Keywords: Artificial intelligence; Bill of materials; Cyber security; Deep learning; Defect detection; Healthcare services; Material defect detection; Medical Devices; Soft-ware defect detection; Software bill of material; Artificial Intelligence (AI); Cybersecurity; Deep Learning; Medical Device; SBOM; Soft-ware Defect Detection