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              <text>Pramila, R.M.; Shukla, Samiksha</text>
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              <text>Risk-Based Authentication System Using Hierarchical Sub-Feature-Based Model-(HSFBM)</text>
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              <text>01-01-2025</text>
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              <text>Lecture Notes in Networks and Systems;Volume;1363 LNNS;pp.493-504</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-981-96-5217-4_39" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-96-5217-4_39&lt;/a&gt; &lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/pages/publications/105015971756?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105015971756?origin=resultslist&lt;/a&gt;</text>
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              <text>Pramila R.M., CHRIST (Deemed to Be University) Pune Lavas, Lavasa, India; Shukla S., CHRIST (Deemed to Be University) Bangalore, Bangalore, India</text>
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              <text>Password-based authentication system recently has been more secure as risk-based authentication system (RBA) is indentured. The RBA system monitors the parameters extracted during the user login process, and based on the proposed model, the system raises a multi-factor authentication to the user. As the vulnerability has increased concerning passwords, fingerprints easy access to any web application may result in a security flow. Several best practices have addressed these issues, but the security threats have been challenging during the initial login sessions. Hence, this paper proposes a novel method for an effective risk identification method during the initial login phase using a hierarchical sub-feature-based model for different categories of users in an RBA system. The FAR is comparatively better in our proposed model, with minimal re-authentication requests for the user.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.</text>
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              <text>Adaptive authentication; Authentication; Contextual information; Risk-based authentication</text>
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              <text>Springer Science and Business Media Deutschland GmbH</text>
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              <text>ISSN: 23673370; ISBN: 978-981965216-7;</text>
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              <text>Restricted Access; Hardcopy may be available in the library</text>
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