인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
The fast-paced digitization and the adoption of cloud-based services have greatly widened the enterprise attack surface and put the networks at risk from internal vulnerabilities as well as external attacks. The traditional model of perimeter-based security cannot adequately protect the modern distributed infrastructure where hybrid work, Bring-Your-Own-Device (BYOD) policies, and Internet-of-Things (IoT) ecosystems are prevalent. This paper presents an approach to a Zero Trust Security Architecture (ZTA) by integrating Federated Learning (FL) in order to provide adaptive, privacy-preserving, and intelligent network protection. The proposed solution allows the ZTA's "never trust, always verify" principle to be enforced while implementing federated learning in order to train AI models collaboratively amongst multiple distributed nodes and without exchanging raw data, thus ensuring confidentiality. Centralized federated learning is used for secure authentication, and Distributed federated learning systems are used for independent constant diagnostics and policy updates. The proposed mathematical formulation and performance comparisons have shown that the combined frameworks can enhance data privacy, scalability, and resiliency while aligning with contemporary Cyber Security Regulatory compliance. We also provide the groundwork for integrating with blockchain and edge optimized AI systems in future work.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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