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    G-RAT | A novel graphical randomized authentication technique for consumer smart devices

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    Date
    2019
    Author
    Khan M.A.
    Ud Din I.
    Jadoon S.U.
    Khan M.K.
    Guizani M.
    Awan K.A.
    ...show more authors ...show less authors
    Metadata
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    Abstract
    User authentication is the process that is exercised millions of times around the globe by using different techniques and methods. The most prominent way of authentication is alphanumerical password forms that have been used for decades. Authorized access is becoming a challenging issue because of the introduction of modern technologies. In addition, traditional alphanumerical passwords have significant security issues, for example, humans forget the combination of keys due to the selection of a difficult key combination. Moreover, when they choose an easy key combination, this helps hackers to crack their passwords easily. Traditional passwords are also vulnerable to several types of attacks, for example, dictionary attack, brute force attack, and malware. To provide an easy and more secure authentication technique, a graphical password has been introduced in this paper for consumer electronic devices, which uses an image or a set of images for authentication. We have categorized the existing graphical password methods into recognition-based, cued-recall-based, pure-recall-based, and hybrid techniques. Due to the limitations of the existing graphical passwords, we have introduced a new technique, named graphical random authentication technique (gRAT), which generates a randomized set of images every time a user tries to authenticate him/herself by maintaining the security and usability at the same time. The gRAT technique is also tested by user-centric evaluation in terms of security, usability, usefulness, and utility, and the experimental results show that the proposed technique is more secure and useful in the real-life authentication applications.
    DOI/handle
    http://dx.doi.org/10.1109/TCE.2019.2895715
    http://hdl.handle.net/10576/13770
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    • Computer Science & Engineering [‎2428‎ items ]

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