Dynamic User Verification Using Touch Keystroke Based on Medians Vector Proximity

Alghamdi, Shatha; Elrefaei, Lamiaa A.

doi:10.1109/cicsyn.2015.31

Cited by 29 publications

(28 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During the interaction with touch screen, there are multiple records which could be generated for the gesture representing the touch event as shown in the following table [28]. Features extracted for every touch event are time of the event, pressure exerted on the screen, position (x and y coordinate), and area of touch.…”

Section: Touch Dynamic Feature Extractionmentioning

confidence: 99%

Performance Analysis for a Touch Dynamic Authentication System with Reduced Feature Set Using Neural Networks

SenthilPrabha

Vidhyapriya

2015

IETE Journal of Research

View full text Add to dashboard Cite

Increase in the usage of smart phones increases the burden for the users to memorize many passwords. It has also increased the need for stronger or enhanced authentication mechanisms. Littlemore innovation labs are providing paperless solution for conducting examinations using touch pad that is believed to be a promising technology in future. This paper explores the suitability of using touch dynamics as an additional level of security during authentication. Both security and performance concerns are investigated. A security application developed can be used on a touch screen device capable of imitating someone's typing characteristics. When the user starts interacting with the device, the developed application starts capturing the behavioural features given by the user's swipe. Thirty different features are captured, from which, most prominent features are identified by depending on the usefulness of the feature. Good results with the reduced feature set are obtained, thereby improving the performance of the system using neural network techniques. The results show that touch dynamics on a smart phone are more durable against certain attacks on a personal computer.

show abstract

Section: Touch Dynamic Feature Extractionmentioning

confidence: 99%

Performance Analysis for a Touch Dynamic Authentication System with Reduced Feature Set Using Neural Networks

SenthilPrabha

Vidhyapriya

2015

IETE Journal of Research

View full text Add to dashboard Cite

show abstract

“…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%

“…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%

“…Authentication using password is the most commonly used method for both local and remote Internet access. Most users use the Internet, even for systems which have critical and sensitive data …”

Section: Introductionmentioning

confidence: 99%

“…Most users use the Internet, even for systems which have critical and sensitive data. 1,2 User's authentication is still the main process in applications that require securing access to sensitive data. It is simply the process of determining whether someone is the one who claims to be.…”

Section: Introductionmentioning

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

User Keystroke Authentication and Recognition of Emotions Based on Convolutional Neural Network

Tereikovskyi

Tereikovska

Korystin³

et al. 2020

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Dynamic User Verification Using Touch Keystroke Based on Medians Vector Proximity

Cited by 29 publications

References 7 publications

Performance Analysis for a Touch Dynamic Authentication System with Reduced Feature Set Using Neural Networks

Performance Analysis for a Touch Dynamic Authentication System with Reduced Feature Set Using Neural Networks

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

User Keystroke Authentication and Recognition of Emotions Based on Convolutional Neural Network

Contact Info

Product

Resources

About