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              <text>Protection Against SIM Swap Attacks on OTP System</text>
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              <text>Authentication; Biometrics; Keystroke dynamics; Machine learning models; OTP; Password; Password-less authentication; Security; Supervised learning; Unsupervised learning</text>
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              <text>One-time password-based authentication stands out to be the most effective in the cluster of password-less authentication systems. It is possible to use it as an authentication factor for login rather than an account recovery mechanism. Recent studies show that attacks like SIM swap and device theft raise a significant threat for the system. In this paper, a new security system is proposed to prevent attacks like SIM swap on OTP systems, the system contains a risk engine made up of supervised and unsupervised machine learning model blocks trained using genuine user data space, and the final decision of the system is subject to a decision block that works on the principles of voting and logic of an AND gate. The proposed system performed well in detecting fraud users, proving the systems significance in solving the problems faced by an OTP system.  2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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              <text>Varghese E.; Pramila R.M.</text>
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              <text>Lecture Notes in Networks and Systems, Vol-462, pp. 219-228.</text>
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              <text>Springer Science and Business Media Deutschland GmbH</text>
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              <text>2022-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-981-19-2211-4_19" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-19-2211-4_19&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135076642&amp;amp;doi=10.1007%2F978-981-19-2211-4_19&amp;amp;partnerID=40&amp;amp;md5=c7d9ec875e6862d94c4065a3f5f64e6a" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135076642&amp;amp;doi=10.1007%2f978-981-19-2211-4_19&amp;amp;partnerID=40&amp;amp;md5=c7d9ec875e6862d94c4065a3f5f64e6a&lt;/a&gt;</text>
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              <text>ISSN: 23673370; ISBN: 978-981192210-7</text>
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              <text>Varghese E., Christ University, Bangalore, India; Pramila R.M., Christ University, Bangalore, India</text>
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