Improving Performance and Usability in Mobile Keystroke Dynamic Biometric Authentication

Alshanketi, Faisal; Traoré, Issa; Ahmed, Ahmed Awad

doi:10.1109/spw.2016.12

Cited by 41 publications

(28 citation statements)

References 9 publications

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“…The main two event times are: pressing time, which is the time stamp when the key is held down (D), and the releasing time, which is the timestamp when the key is released up (U). A signature of user typing rhythm could be extracted by capturing all time lengths of each event . These time lengths are described in Table shown below.…”

Section: Background Informationmentioning

confidence: 99%

“…Other features are finger pressure, position on key, and size of key surface touched by finger. All these features may change during the duration period of a key when pressed or released, and can be a source for multiple values for the same feature , . Hence, we have extracted them each time we press or release the key and determined which is to be recorded in the typing rhythm features.…”

Section: Background Informationmentioning

confidence: 99%

“…Pressure is a measure of the force applied by the finger on the touch screen (button). 1,5,7 Position This value is being described using X, Y coordinates. For each key, and have values between 0 and maximum screen coordinates.…”

Section: Pressurementioning

confidence: 99%

“…User authentication is achieved by something that the user knows (eg, password) or has (eg, credit card, iris sacs, finger prints, hand geometry). Recently, a new trend for user authentication is based on how he/she behaves (eg, typing behavior) . User authentication by passwords is a reliable and efficient access control procedure; however, it is not a sufficient way to use just a password because it is subject to many threats and issues …”

Section: Introductionmentioning

confidence: 99%

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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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Section: Background Informationmentioning

confidence: 99%

Section: Background Informationmentioning

confidence: 99%

Section: Pressurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Salem

Obaidat

2019

Security and Privacy

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“…More recently, Alshanketi et al proposed a model for keystroke dynamic analysis for authentication on smartphones. In addition, they improved the usability and accuracy of mobile Keystroke Dynamic by introducing an algorithm for handling typos.…”

Section: Emerging Biometric Technologiesmentioning

confidence: 99%

State of the art and perspectives on traditional and emerging biometrics: A survey

Traoré

Alshahrani

Obaidat

2018

Security and Privacy

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The last three decades have seen a shift and impressive progress in the biometric technologies landscape. Several major real‐world applications of biometrics have been released and are currently being used around the world. At the same time, several new biometrics modalities have emerged and have started making an impact in securing sensitive data, information, and systems. We present in this paper a survey of traditional and emerging biometrics by emphasizing their connection to human body parts, behavior, and cognition. A review of the state of the art in the key technologies is carried out along with a discussion of major applications currently available in the real world.

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Window Functions in Rhythm Based Biometric Authentication

Alpar

Krejcar

2020

Bioinformatics and Biomedical Engineering

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Improving Performance and Usability in Mobile Keystroke Dynamic Biometric Authentication

Cited by 41 publications

References 9 publications

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

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

State of the art and perspectives on traditional and emerging biometrics: A survey

Window Functions in Rhythm Based Biometric Authentication

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