메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술대회자료
저자정보
Chul Min Kim (Korea Advanced Institute of Science and Technology) Jung Hwan Kim (Korea Advanced Institute of Science and Technology) Man-Sung Yim (Korea Advanced Institute of Science and Technology)
저널정보
대한인간공학회 대한인간공학회 학술대회논문집 2019 대한인간공학회 추계학술대회
발행연도
2019.10
수록면
46 - 49 (4page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

초록· 키워드

오류제보하기
Objective: The aim of this study is to investigate the feasibility of using Electroencephalogram (EEG) to predict and thereby minimize the risk of potential insider threat at nuclear power plants (NPPs). Background: In nuclear facilities, insider threats are perhaps the most serious challenges that security systems face. Trustworthiness assessment and security culture is key components to reduce insider threat. However, trustworthy assessment and security culture is subjective, which needs an objective methodology to make up for. Method: The subjects read the insider threat scenarios and selected whether they would be an insider or not. Scenarios include various theft and sabotage scenarios, and roles in NPPs. EEG signal is measured during the reading and deciding process. Data were obtained from 25 subjects (6 women), aged 19 to 31 years. Preprocessing includes down-sampling, automatic noise removal, and artifact rejection using ICA. Random forest was used to select features, and KNN was used to categorize yes/no answers. Results: Classification accuracy was 70~73% depending on the variation of preprocessing, feature selection and parameter tuning methodologies. Conclusion: This research investigated the use of EEG signal to identify potential insider threat at nuclear facilities. Application: This methodology is shown to be useful in that they provide opportunities to identify potential insider threat.

목차

ABSTRACT
1. Introduction
2. Method
3. Results and Discussion
4. Conclusion
References

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0