Keystroke Biometric Systems for User Authentication

Ali, Liakat; Monaco, John V.; Tappert, Charles C.; Qiu, Meikang

doi:10.1007/s11265-016-1114-9

Cited by 89 publications

(45 citation statements)

References 101 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Behavioral rhythm is being characterized using several combinations of features . These features are being extracted and processed from authentication system interfaces.…”

Section: Background Informationmentioning

confidence: 99%

“…In NN models, researchers first build a prediction model from input provided by users during enrollment, which will be later used to predict the outcome of a new trial provided by the user during authentication. Authentication in NN, similar to statistical approach, consists of comparing the users profile by the new input provided in the authentication process , Hybrid techniques…”

Section: Background Informationmentioning

confidence: 99%

“…19,20 Behavioral rhythm is being characterized using several combinations of features. 21,22 These features are being extracted and processed from authentication system interfaces. The interfaces could be opened from several devices such as classical keyboards or built-in software keyboard in touch screen and smart phone and tablet devices.…”

Section: Background Informationmentioning

confidence: 99%

See 2 more Smart Citations

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

Salem

Obaidat

2019

Security and Privacy

View full text Add to dashboard Cite

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.

show abstract

“…Behavioral rhythm is being characterized using several combinations of features . These features are being extracted and processed from authentication system interfaces.…”

Section: Background Informationmentioning

confidence: 99%

Section: Background Informationmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…such as a security electronic token, a key, trusted device, passport, smart cards, or ID card as an Automatic Teller Machine card (ATM card) [21] [22]. Those physical devices were developed to overcome certain weakness associated with using passwords.…”

Section: Possession-based Authentication Techniquesmentioning

confidence: 99%

“…The template of the user is stored in the database server on the cloud for future matching. New researches in 2017 identify some problems to take into considerations when designing a keystroke system and how to improve the accuracy of this system [21]. J Miya's method [67] achieved more than 96.5% accuracy.…”

Section: Keystroke Dynamicsmentioning

confidence: 99%

Biometric-based Authentication Techniques for Securing Cloud Computing Data - A Survey

Asmaa¹,

Hala²,

Mostafa-Sami³

2018

IJCA

View full text Add to dashboard Cite

Cloud Computing is the most growing paradigm for delivering computational resources as a service over the internet. By 2018, many different enterprises have adopted this utility-based computing for reducing the operational and capital expenditure of building their infrastructure network, buying software licenses, hiring IT teams and other requirements. With this unlimited growth of using cloud services and the multi-tenancy nature of sharing cloud service instance between different consumers and enterprises, securing accessing to the data of the cloud became a major issue. Traditional authentication techniques and credentials does not provide enough security against the modern means of attacks. So, new biometric-based authentication techniques have been discovered to overcome the loop holes. Here we present a comprehensive survey on the existing user's authentication techniques especially biometric-based techniques used for accessing cloud data.

show abstract

Elastic‐Beam Triboelectric Nanogenerator for High‐Performance Multifunctional Applications: Sensitive Scale, Acceleration/Force/Vibration Sensor, and Intelligent Keyboard

Chen

Wang

Zhang

et al. 2018

Advanced Energy Materials

113

View full text Add to dashboard Cite

Exploiting novel devices for either collecting energy or self‐powered sensors is vital for Internet of Things, sensor networks, and big data. Triboelectric nanogenerators (TENGs) have been proved as an effective solution for both energy harvesting and self‐powered sensing. The traditional triboelectric nanogenerators are usually based on four modes: contact‐separation mode, lateral sliding mode, single‐electrode mode, and freestanding triboelectric‐layer mode. Since the reciprocating displacement/force is necessary for all working modes, developing efficient elastic TENG is going to be important and urgent. Here, a kind of elastic‐beam TENG with arc‐stainless steel foil is developed, whose structure is quite simple, and its working states depend on the contact area and separating distance as proved by experiments and theoretical calculations. This structure is different from traditional structures, e.g., direct sliding or contact‐separation structures, whose working states mainly depend on contact area or separating distance. This triboelectric nanogenerator shows advanced mechanical and electrical performance, such as high sensitivity, elasticity, and ultrahigh frequency response, which encourage applications as a force sensor, sensitivity scale, acceleration sensor, vibration sensor, and intelligent keyboard.

show abstract

Keystroke Biometric Systems for User Authentication

Cited by 89 publications

References 101 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

Biometric-based Authentication Techniques for Securing Cloud Computing Data - A Survey

Elastic‐Beam Triboelectric Nanogenerator for High‐Performance Multifunctional Applications: Sensitive Scale, Acceleration/Force/Vibration Sensor, and Intelligent Keyboard

Contact Info

Product

Resources

About