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              <text>An Architecture for Risk-Based Authentication System in a Multi-Server Environment</text>
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              <text>Adaptive Authentication(AA); Machine Learning; Multi-Factor Authentication(MFA); Multi-Server Environment; Risk-Based Authentication(RBA)</text>
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              <text>Identity authentication, a vital part of any application access, is also one way for imposters to gain access to an application using various fingerprint authentication technologies. Therefore, because of the lack of security in the authentication architecture, this paper proposes an architecture for a risk-based authentication system using a machine learning model in a multi-server environment. Since the recent study mainly focuses on the multi-server environment and adaptive authentication independently, very little work has been proposed using a multi-server environment for adaptive authentication. The study aims to estimate risk for the user during the initial login process and when the user's data is extracted enough for prediction in a multi-server environment.   2023 IEEE.</text>
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              <text>Pramila R.M.; Shukla S.</text>
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              <text>2023 IEEE International Conference on Public Key Infrastructure and its Applications, PKIA 2023 - Proceedings</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>2023-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1109/PKIA58446.2023.10262513" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/PKIA58446.2023.10262513&lt;/a&gt;
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              <text>Restricted Access</text>
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              <text>ISBN: 979-835031055-9</text>
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              <text>English</text>
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              <text>Pramila R.M., Christ (Deemed to Be University), Data Science Department, Lavasa, Pune, India; Shukla S., Christ (Deemed to Be University), Data Science Department, Lavasa, Pune, India</text>
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