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              <text>A Survey on Adaptive Authentication Using Machine Learning Techniques</text>
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              <text>Behavior-based fingerprinting; Browser fingerprinting; Context-based fingerprinting; Risk-based authentication; Security and privacy</text>
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              <text>Adaptive authentication is a reliable technique to dynamically select the best mechanisms among multiple modalities to authenticate a user based on the users risk profile generated using behavior and context-based information. Websites or enterprise applications enabled with adaptive authentication will have a more robust security system as analyzing the large volume of the user, device, and browser data in real time generates a risk score that decides the appropriate level of security. Though a significant amount of research is being carried out on adaptive authentication, no single model is suitable for a global attack. This paper provides a structured (extensive) survey of current adaptive authentication techniques available in the literature to identify the challenges which demand future research.  2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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              <text>Pramila R.M.; Misbahuddin M.; Shukla S.</text>
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              <text>Lecture Notes in Networks and Systems, Vol-462, pp. 317-335.</text>
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              <text>Springer Science and Business Media Deutschland GmbH</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-981-19-2211-4_28" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-19-2211-4_28&lt;/a&gt;
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              <text>ISSN: 23673370; ISBN: 978-981192210-7</text>
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              <text>Pramila R.M., Christ University, Bangalore, India; Misbahuddin M., Centre for Development and Advanced Computing (CDAC), Electronics City, Bangalore, India; Shukla S., Christ University, Bangalore, India</text>
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