Identification of User Behavioral Biometrics for Authentication Using Keystroke Dynamics and Machine Learning

Krishnamoorthy, Sowndarya; Rueda, Luis; Saad, Sherif; Elmiligi, Haytham

doi:10.1145/3230820.3230829

Cited by 58 publications

(29 citation statements)

References 16 publications

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“…During the authentication phase, the user is supposed to type the same string captured during his enrollment. Changing the password means that we need to enroll the identity again because the methods are not able to work with a different text not being stored earlier in the database …”

Section: Background Informationmentioning

confidence: 99%

“…This text mode verification usually asks the users to type a long text to create the model indirectly by monitoring the user typing rhythm keyboard for a certain period. It is called continuous authentication in literature , …”

Section: Background Informationmentioning

confidence: 99%

“…Static approaches provide more robust user verification than simple passwords, but they do not provide continuous security. Therefore, they cannot detect substitution of the user for identity verification …”

Section: Background Informationmentioning

confidence: 99%

“…For each key, and have values between 0 and maximum screen coordinates. 3,13,31 Size It is a measure of the surface of finger in contact with the key when key down event or key up event are detected. [1][2][3] Finger placement Where the finger is placed on the device or keyboard or even the angel of the finger when pressing the key.…”

Section: Pressurementioning

confidence: 99%

“…It is called continuous authentication in literature. 1,2,18,[31][32][33][34][35][36][37][38] In static method verification, the keystrokes are analyzed only at specific times for example, during login. Static approaches provide more robust user verification than simple passwords, but they do not provide continuous security.…”

Section: Dynamic Textmentioning

confidence: 99%

See 4 more Smart Citations

A novel security scheme for behavioral authentication systems based on keystroke dynamics

Salem

Obaidat

2019

Security and Privacy

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This article presents a comprehensive analysis behavioral authentication systems based on keystroke dynamics using android mobile devices. Behavioral security authentication is an efficient biometric‐based security system that can be used to authenticate users. It is exploited to strengthen password authentication efficiently and inexpensively because no extra hardware is required in most of these schemes. Keystroke dynamic rhythm uses combinations of timing features and nontiming features that are extracted and processed from several devices such as classical keyboard and built‐in software keyboard in touch screen and smart phone devices. This work presents a comprehensive analysis of using biometric behavioral authentication system‐based on keystroke dynamics. Neural network classifiers are used in this work. The performance results show that keystroke dynamics provides good level in performance measures as a second authentication factor. The distinguishable role for nontiming features in addition to the timing features is demonstrated. These features have a significant role in improving the performance measures. The proposed model achieves low error rate of 0.3% for false acceptance rate, a false rejection of 1.5%, and an equal error rate of 0.9%. These are considered excellent enhancements when compared with previous reported results. Suggestions for future directions, and challenges for using behavioral‐based authentication systems are also highlighted.

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