Psychomotor Impairment Detection via Finger Interactions with a Computer Keyboard During Natural Typing

Modern digital devices and appliances are capable of monitoring the timing of button presses, or finger interactions in general, with a sub-millisecond accuracy. However, the massive amount of high resolution temporal information that these devices could collect is currently being discarded. Multiple studies have shown that the act of pressing a button triggers well defined brain areas which are known to be affected by motor-compromised conditions. In this study, we demonstrate that the daily interaction with a computer keyboard can be employed as means to observe and potentially quantify psychomotor impairment. We induced a psychomotor impairment via a sleep inertia paradigm in 14 healthy subjects, which is detected by our classifier with an Area Under the ROC Curve (AUC) of 0.93/0.91. The detection relies on novel features derived from key-hold times acquired on standard computer keyboards during an uncontrolled typing task. These features correlate with the progression to psychomotor impairment (p < 0.001) regardless of the content and language of the text typed, and perform consistently with different keyboards. The ability to acquire longitudinal measurements of subtle motor changes from a digital device without altering its functionality may allow for early screening and follow-up of motor-compromised neurodegenerative conditions, psychological disorders or intoxication at a negligible cost in the general population.Comunidad de Madri

Credential hardening by using touchstroke dynamics

Today, reliance on digital devices for daily routines has been shifted towards portable mobile devices. Therefore, the need for security enhancements within this platform is imminent. Numerous research works have been performed on strengthening password authentication by using keystroke dynamics biometrics, which involve computer keyboards and cellular phones as input devices. Nevertheless, experiments performed specifically on touch screen devices are relatively lacking. This paper describes a novel technique to strengthen security authentication systems on touch screen devices via a new sub variant behavioural biometrics called touchstroke dynamics. We capitalize on the high resolution timing latency and the pressure information on touch screen panel as feature data. Following this a light weight algorithm is introduced to calculate the similarity between feature vectors. In addition, a fusion approach is proposed to enhance the overall performance of the system to an equal error rate of 7.71% (short input) and 6.27% (long input)

Keystroke Dynamics Authentication For Collaborative Systems

We present in this paper a study on the ability and the benefits of using a keystroke dynamics authentication method for collaborative systems. Authentication is a challenging issue in order to guarantee the security of use of collaborative systems during the access control step. Many solutions exist in the state of the art such as the use of one time passwords or smart-cards. We focus in this paper on biometric based solutions that do not necessitate any additional sensor. Keystroke dynamics is an interesting solution as it uses only the keyboard and is invisible for users. Many methods have been published in this field. We make a comparative study of many of them considering the operational constraints of use for collaborative systems

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

Keystroke Biometrics for Freely Typed Text Based on CNN model

Keystroke biometrics, as an authentication method with advantages of no extra hardware cost, easy-to-integrate and high-security, has attracted much attention in user authentication. However, a mass of researches on keystroke biometrics have focused on the fixed-text analysis, while only a few took free-text analysis into consideration. And in the field of free-text analysis, most researchers usually devote their efforts to extracting the most appropriate keystroke features on their own experience. These methods were inevitably questionable due to their strong subjectivity. In this paper we proposed a multi-user keystroke authentication scheme based on CNN model, which can automatically figure out the appropriate features for the model, adjust and optimize the model constantly to further enhance the performance of model. In the experiment on a small sample set, the performance is improved more than 10% compared with the benchmark. Our model achieves an average recognition accuracy of 92.58%, with FAR of 0.24% and FRR of 7.34%

Psychomotor Impairment Detection via Finger Interactions with a Computer Keyboard During Natural Typing

Modern digital devices and appliances are capable of monitoring the timing of button presses, or finger interactions in general, with a sub-millisecond accuracy. However, the massive amount of high resolution temporal information that these devices could collect is currently being discarded. Multiple studies have shown that the act of pressing a button triggers well defined brain areas which are known to be affected by motor-compromised conditions. In this study, we demonstrate that the daily interaction with a computer keyboard can be employed as means to observe and potentially quantify psychomotor impairment. We induced a psychomotor impairment via a sleep inertia paradigm in 14 healthy subjects, which is detected by our classifier with an Area Under the ROC Curve (AUC) of 0.93/0.91. The detection relies on novel features derived from key-hold times acquired on standard computer keyboards during an uncontrolled typing task. These features correlate with the progression to psychomotor impairment (p < 0.001) regardless of the content and language of the text typed, and perform consistently with different keyboards. The ability to acquire longitudinal measurements of subtle motor changes from a digital device without altering its functionality may allow for early screening and follow-up of motor-compromised neurodegenerative conditions, psychological disorders or intoxication at a negligible cost in the general population

Psychomotor Impairment Detection via Finger Interactions with a Computer Keyboard During Natural Typing

Modern digital devices and appliances are capable of monitoring the timing of button presses, or finger interactions in general, with a sub-millisecond accuracy. However, the massive amount of high resolution temporal information that these devices could collect is currently being discarded. Multiple studies have shown that the act of pressing a button triggers well defined brain areas which are known to be affected by motor-compromised conditions. In this study, we demonstrate that the daily interaction with a computer keyboard can be employed as means to observe and potentially quantify psychomotor impairment. We induced a psychomotor impairment via a sleep inertia paradigm in 14 healthy subjects, which is detected by our classifier with an Area Under the ROC Curve (AUC) of 0.93/0.91. The detection relies on novel features derived from key-hold times acquired on standard computer keyboards during an uncontrolled typing task. These features correlate with the progression to psychomotor impairment (p < 0.001) regardless of the content and language of the text typed, and perform consistently with different keyboards. The ability to acquire longitudinal measurements of subtle motor changes from a digital device without altering its functionality may allow for early screening and follow-up of motor-compromised neurodegenerative conditions, psychological disorders or intoxication at a negligible cost in the general population

Improving web authentication with keystroke dynamics

Dissertação de mestrado em Engenharia InformáticaAuthentication is frequently referred as the most critical part of a computer system security. Users commonly identify themselves using a combination of username and password, but sometimes this is not enough. Concerning web-based services, attacks like phishing or social engineering can easily result in identity theft. In addition, the widespread use of single sign-on services can seriously increase the consequences of such attacks. In these circumstances strong authentication is mandatory. Strong authentication is often implemented using additional authentication steps or specialized hardware modules, which is not suitable for web-based systems. However, biometrics can used to overcome these limitations. More specifically, behavioural biometrics based on keyboard typing patterns can provide an extra security layer on top of conventional authentication methods, with no additional cost and no impact to the user experience. This work aims to evaluate the feasibility of the implementation of strong authentication on the web using keystroke dynamics. This is carried out through the creation of a web-application prototype, collection of a keystroke dynamics dataset and analysis of various matching algorithms and performance metrics on the collected data.O processo de autenticação é frequentemente referido como a parte mais importante da segurança de um sistema informático. Normalmente, os utilizadores identificam-se utilizando nome de utilizador e palavra-passe, mas este mecanismo nem sempre é suficiente. Considerando serviços baseados na web, ataques como phishing ou engenharia social podem facilmente levar ao roubo de identidade. Para além disso, a utilização crescente de serviços de single sign-on apresenta novos riscos e consequências deste tipo de ataques. Nestas circunstâncias a autenticação forte é fundamental. A autenticação forte é tipicamente implementada por meio de passos adicionais de autenticação ou módulos de hardware especializado, o que não é adequado a sistemas baseados na web. No entanto, biometrias podem ser usadas para ultrapassar estas limitações. Mais especificamente, biometrias comportamentais baseadas em padrões de digitação no teclado podem fornecer um nível de segurança adicional, sem custo acrescido ou impacto na experiência de utilização. Este trabalho tem como objetivo avaliar a viabilidade da implementação de autenticação forte na web usando dinâmica de digitação. Isto é conseguido através da implementação de um protótipo sob a forma de uma aplicação web, captura de dados de digitação e análise de vários algoritmos e métricas de desempenho sobre os dados recolhidos

Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications