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논문 기본 정보

자료유형
학술대회자료
저자정보
Rin Nadia (Sejong University) Dana Koshen (Sejong University) JaeSeung Song (Sejong University)
저널정보
한국통신학회 한국통신학회 학술대회논문집 2020년도 한국통신학회 추계종합학술발표회 논문집
발행연도
2020.11
수록면
204 - 207 (4page)

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Internet of Things (IoT) user authentication is an essential part to keep and IoT ecosystem from an intruder. Due to this motivation, this authentication system has drawn researchers’ interest. Light, simple and pleasant in user experience are the ideal criteria for the authentication system. Compare to non-biometric security identifier, the biometric identifier naturally has characteristics to build a strong but convenient personal authentication system. However, the typical biometric system employs template matcher to verify user. For certain biometric data which is prone to deviation of biometric features caused by body changing, the template matcher can not learn the pattern alteration. As an application of Artificial Intelligence, machine learning is designed to get this shifting pattern. The usage of machine learning as template matcher substitute could create a flexible, secure but simple authentication system. Human impedance is one of the biometric data and its usability as authentication factor has been proven in several studies. Therefore, by conducting the experiment involving twenty five machine learning algorithms, this work proposes best binary classifier candidates for user verification in IoT ecosystem using human impedance.

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Abstract
Ⅰ. INTRODUCTION
Ⅱ. RELATED WORK
Ⅲ. EXPERIMENTAL SET UP
Ⅳ. EVALUATION METHODOLOGY
Ⅴ. RESULT AND CLASSIFICATION ANALYSIS
Ⅵ. CONCLUSION
Ⅶ. ACKNOWLEDGMENT
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