80 research outputs found

    Combined scaled manhattan distance and mean of horner’s rules for keystroke dynamic authentication

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    Account security was determined by how well the security techniques applied by the system were used. There had been many security methods that guaranteed the security of their accounts, one of which was Keystroke Dynamic Authentication. Keystroke Dynamic Authentication was an authentication technique that utilized the typing habits of a person as a security measurement tool for the user account. From several research, the average use in the Keystroke Dynamic Authentication classification is not suitable, because a user's typing speed will change over time, maybe faster or slower depending on certain conditions. So, in this research, we proposed a combination of the Scaled Manhattan Distance method and the Mean of Horner's Rules as a classification method between the user and attacker against the Keystroke Dynamic Authentication. The reason for using Mean of Horner’s Rules can adapt to changes in values over time and based on the results can improve the accuracy of the previous method

    Biometric Systems

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    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

    Biometrics

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    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

    Continuous user authentication featuring keystroke dynamics based on robust recurrent confidence model and ensemble learning approach

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    User authentication is considered to be an important aspect of any cybersecurity program. However, one-time validation of user’s identity is not strong to provide resilient security throughout the user session. In this aspect, continuous monitoring of session is necessary to ensure that only legitimate user is accessing the system resources for entire session. In this paper, a true continuous user authentication system featuring keystroke dynamics behavioural biometric modality has been proposed and implemented. A novel method of authenticating the user on each action has been presented which decides the legitimacy of current user based on the confidence in the genuineness of each action. The 2-phase methodology, consisting of ensemble learning and robust recurrent confidence model(R-RCM), has been designed which employs a novel perception of two thresholds i.e., alert and final threshold. Proposed methodology classifies each action based on the probability score of ensemble classifier which is afterwards used along with hyperparameters of R-RCM to compute the current confidence in the genuineness of user. System decides if user can continue using the system or not based on new confidence value and final threshold. However, it tends to lock out imposter user more quickly if it reaches the alert threshold. Moreover, system has been validated with two different experimental settings and results are reported in terms of mean average number of genuine actions (ANGA) and average number of imposter actions(ANIA), whereby achieving the lowest mean ANIA with experimental setting II

    Continuous user authentication featuring keystroke dynamics based on robust recurrent confidence model and ensemble learning approach

    Get PDF
    User authentication is considered to be an important aspect of any cybersecurity program. However, one-time validation of user’s identity is not strong to provide resilient security throughout the user session. In this aspect, continuous monitoring of session is necessary to ensure that only legitimate user is accessing the system resources for entire session. In this paper, a true continuous user authentication system featuring keystroke dynamics behavioural biometric modality has been proposed and implemented. A novel method of authenticating the user on each action has been presented which decides the legitimacy of current user based on the confidence in the genuineness of each action. The 2-phase methodology, consisting of ensemble learning and robust recurrent confidence model(R-RCM), has been designed which employs a novel perception of two thresholds i.e., alert and final threshold. Proposed methodology classifies each action based on the probability score of ensemble classifier which is afterwards used along with hyperparameters of R-RCM to compute the current confidence in the genuineness of user. System decides if user can continue using the system or not based on new confidence value and final threshold. However, it tends to lock out imposter user more quickly if it reaches the alert threshold. Moreover, system has been validated with two different experimental settings and results are reported in terms of mean average number of genuine actions (ANGA) and average number of imposter actions(ANIA), whereby achieving the lowest mean ANIA with experimental setting II

    Biometrics

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    Biometrics-Unique and Diverse Applications in Nature, Science, and Technology provides a unique sampling of the diverse ways in which biometrics is integrated into our lives and our technology. From time immemorial, we as humans have been intrigued by, perplexed by, and entertained by observing and analyzing ourselves and the natural world around us. Science and technology have evolved to a point where we can empirically record a measure of a biological or behavioral feature and use it for recognizing patterns, trends, and or discrete phenomena, such as individuals' and this is what biometrics is all about. Understanding some of the ways in which we use biometrics and for what specific purposes is what this book is all about
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