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Anomaly detection through keystroke and tap dynamics implemented via machine learning algorithms.

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Title: Anomaly detection through keystroke and tap dynamics implemented via machine learning algorithms.
Authors: JAWED, Hani1, ZIAD, Zara1, KHAN, Muhammad Mubashir1 mmkhan@neduet.edu.pk, ASRAR, Maheen1
Source: Turkish Journal of Electrical Engineering & Computer Sciences. 2018, Vol. 26 Issue 4, p1698-1709. 12p.
Subjects: Anomaly detection (Computer security), Keystroke timing authentication, Machine learning, Computer passwords, Windows (Graphical user interfaces), Security systems
Abstract: In our world of growing machine intelligence and increasing security risks, there is a dire need for authentication to be liberated from password dependency and restrictions. This paper discusses the implementation of keystroke biometrics to enhance security using machine-learning algorithms on both Windows and Android. Our research analyzes a user's behavior for authorization purposes by capturing the user's typing pattern. The system extracts several features from the user's typing pattern to apply unary classification for user behavior analysis so that we can detect unauthorized users. Our system implements machine learning on tap dynamics in Android, allowing both training and prediction and overcoming its computational restrictions. [ABSTRACT FROM AUTHOR]
Copyright of Turkish Journal of Electrical Engineering & Computer Sciences is the property of Scientific and Technical Research Council of Turkey and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Anomaly detection through keystroke and tap dynamics implemented via machine learning algorithms.
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  Data: <searchLink fieldCode="AR" term="%22JAWED%2C+Hani%22">JAWED, Hani</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22ZIAD%2C+Zara%22">ZIAD, Zara</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22KHAN%2C+Muhammad+Mubashir%22">KHAN, Muhammad Mubashir</searchLink><relatesTo>1</relatesTo><i> mmkhan@neduet.edu.pk</i><br /><searchLink fieldCode="AR" term="%22ASRAR%2C+Maheen%22">ASRAR, Maheen</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Turkish+Journal+of+Electrical+Engineering+%26+Computer+Sciences%22">Turkish Journal of Electrical Engineering & Computer Sciences</searchLink>. 2018, Vol. 26 Issue 4, p1698-1709. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Anomaly+detection+%28Computer+security%29%22">Anomaly detection (Computer security)</searchLink><br /><searchLink fieldCode="DE" term="%22Keystroke+timing+authentication%22">Keystroke timing authentication</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+passwords%22">Computer passwords</searchLink><br /><searchLink fieldCode="DE" term="%22Windows+%28Graphical+user+interfaces%29%22">Windows (Graphical user interfaces)</searchLink><br /><searchLink fieldCode="DE" term="%22Security+systems%22">Security systems</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: In our world of growing machine intelligence and increasing security risks, there is a dire need for authentication to be liberated from password dependency and restrictions. This paper discusses the implementation of keystroke biometrics to enhance security using machine-learning algorithms on both Windows and Android. Our research analyzes a user's behavior for authorization purposes by capturing the user's typing pattern. The system extracts several features from the user's typing pattern to apply unary classification for user behavior analysis so that we can detect unauthorized users. Our system implements machine learning on tap dynamics in Android, allowing both training and prediction and overcoming its computational restrictions. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Turkish Journal of Electrical Engineering & Computer Sciences is the property of Scientific and Technical Research Council of Turkey and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3906/elk-1711-410
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      – Code: eng
        Text: English
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        PageCount: 12
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      – SubjectFull: Anomaly detection (Computer security)
        Type: general
      – SubjectFull: Keystroke timing authentication
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Computer passwords
        Type: general
      – SubjectFull: Windows (Graphical user interfaces)
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      – SubjectFull: Security systems
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      – TitleFull: Anomaly detection through keystroke and tap dynamics implemented via machine learning algorithms.
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            NameFull: KHAN, Muhammad Mubashir
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            – D: 01
              M: 08
              Text: 2018
              Type: published
              Y: 2018
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              Value: 26
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            – TitleFull: Turkish Journal of Electrical Engineering & Computer Sciences
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