| Project/Area Number |
19J15225
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Single-year Grants |
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| Section | 国内 |
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| Review Section |
Basic Section 60070:Information security-related
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| Research Institution | The University of Aizu |
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Principal Investigator |
ZHOU LU 会津大学, コンピュータ理工学研究科, 特別研究員(PD)
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| Project Period (FY) |
2019-04-25 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥1,900,000 (Direct Cost: ¥1,900,000)
Fiscal Year 2020: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2019: ¥1,000,000 (Direct Cost: ¥1,000,000)
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| Keywords | secure authentication / support vector machine / fuzzy rough sets theory / IoT Security / Authentication in IoT |
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| Outline of Research at the Start |
Our research upgrades the security primitives for IoT devices and contributes to the popularization of IoT devices based service and application, which will bring tremendous social and economic impacts. It also will promote various ways for big data utilization as emerging requirements in various industries.
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| Outline of Annual Research Achievements |
In this fiscal year, we combined vector machine algorithms with fuzzy rough sets theory in our secure user biometrics authentication, which is more secure than traditional FaceID and TouchID. Traditional core vector machine (CVM) and support vector machine (SVM) have some limitations when used for data classification, while the addition of fuzzy rough sets theory can dynamically adjust the degree of the membership function, optimizing the weight distribution of each feature, and further improving the classification accuracy.
We developed a new algorithm combined with SVM and fuzzy rough sets are used to train and identify malicious domain generated by domain generation algorithms combined SVM with fuzzy rough sets, using online and incremental algorithms to automatically identify and classify non-existent domains as benign or malicious. Experiments show that the algorithm can indeed achieve a high classification accuracy, reaching more than 99%.
We also develop a new algorithm combined with CVM and fuzzy rough sets used to train and identify users who login and authenticate through biometric and behavioral characteristics. Our application makes the medical cloud bring more convenience to share medical data within the same hospital or between different hospitals and more secure to unauthorized access. We obtain biological and behavioral characteristics from doctors' own gestures for training and classifying, to ensure that only authorized doctors can access patient data.
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| Research Progress Status |
令和2年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
令和2年度が最終年度であるため、記入しない。
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