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
Abstract False data injection attack in smart grid might does not launch interference and attack behaviors, so that this attack is difficult found. To address this, this paper proposed a conformal neural network detection method being sensitive to false data. Firstly, using the conformity scores calculated by the mathematical probability to identify the false data. Then, the neural network learns a boundary separating false data and normal data on the conformal region yielded by the conformity scores. Finally, experiments on simulated and real datasets indicate that the proposed method obtains 0.9738 detected accuracy, and the sensitivity to false data reaches 0.9387, defeating against the competitors, moreover, the proposed method does not exhibit exponential detection time as data volume augments. We demonstrate that evaluating the consistency between the data does not rely on data distributions and the operation status in this real-time system like smart grids, since conformity scores can calculate the mathematical probability following the same data distribution. The boundaries learned from conformal regions are independent of data distribution and the information of operation status in smart grids. This evaluation manner of data consistency and that of boundary learning are equally applicable to the identification and separation of those false data injected into other real-time systems.
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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