One-class naïve Bayes with duration feature ranking for accurate user authentication using keystroke dynamics

Ho, Jiacang; Kang, Dae-Ki

doi:10.1007/s10489-017-1020-2

Cited by 17 publications

(24 citation statements)

References 27 publications

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“…These features are divided into two main classes: timing features and nontiming features. A brief description of each is given next …”

Section: Background Informationmentioning

confidence: 99%

“…Each research work reported in the literature was dedicated to the use of a specific text mode for data entry. There are two main text mode entries: static and dynamic …”

Section: Background Informationmentioning

confidence: 99%

“…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%

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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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“…These features are divided into two main classes: timing features and nontiming features. A brief description of each is given next …”

Section: Background Informationmentioning

confidence: 99%

“…Each research work reported in the literature was dedicated to the use of a specific text mode for data entry. There are two main text mode entries: static and dynamic …”

Section: Background Informationmentioning

confidence: 99%

See 1 more Smart Citation

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

Salem

Obaidat

2019

Security and Privacy

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“…Many people carry out daily activities through the Internet, such as money transactions, file transfers, gathering information, and chatting [1]. Each activity carried out requires a unique identifier (ID) and password to verify an account that is used for authentication on the system.…”

Section: Introductionmentioning

confidence: 99%

“…Researches [5] [6] used the average for the KDA classification. The use of average according to Ho and Kang [1] research is not suitable for data streams such as KDA, so it is proposed to use the mean of Horner's Rule (MHR) that is suitable for data streams. Based on [1], this research proposed to use MHR for the classification of KDA and combined it with a method that was used by Gupta & Gopal [5] and Yang & Fang [6].…”

Section: Introductionmentioning

confidence: 99%

Keystroke Dynamic Authentication Using Combined MHR (Mean of Horner’s Rules) and Standard Deviation

Chandranegara

Sumadi

2018

KINETIK

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Keystroke Dynamic Authentication used a behavior to authenticate the user and one of biometric authentication. The behavior used a typing speed a character on the keyboard and every user had a unique behavior in typing. To improve classification between user and attacker of Keystroke Dynamic Authentication in this research, we proposed a combination of MHR (Mean of Horner’s Rules) and standard deviation. The results of this research showed that our proposed method gave a high accuracy (93.872%) than the previous method (75.388% and 75.156%). This research gave an opportunity to implemented in real login system because our method gave the best results with False Acceptance Rate (FAR) is 0.113. The user can be used as a simple password and ignore a worrying about an account hacking in the system.

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An Improved Method of Time-Frequency Joint Analysis of Mouse Behavior for Website User Trustworthy Authentication

et al. 2020

HCI for Cybersecurity, Privacy and Trust

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One-class naïve Bayes with duration feature ranking for accurate user authentication using keystroke dynamics

Cited by 17 publications

References 27 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

Keystroke Dynamic Authentication Using Combined MHR (Mean of Horner’s Rules) and Standard Deviation

An Improved Method of Time-Frequency Joint Analysis of Mouse Behavior for Website User Trustworthy Authentication

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