A Survey of Machine Learning Techniques for Behavioral-Based Biometric User Authentication

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spelling 14467 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=14467 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Book Chapter application/pdf 26 1.6 Adobe Acrobat Pro DC 20 Paper Capture Plug-in Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML like Gecko) Chrome/71.0.3578.98 Safari/537.36 2024-08-30 11:10:59 3860-01-FH05-FIK-19-22886.pdf UniSZA Private Access A Survey of Machine Learning Techniques for Behavioral-Based Biometric User Authentication Authentication is a way to enable an individual to be uniquely identified usually based on passwords and personal identification number (PIN). The main problems of such authentication techniques are the unwillingness of the users to remember long and challenging combinations of numbers, letters, and symbols that can be lost, forged, stolen, or forgotten. In this paper, we investigate the current advances in the use of behavioral-based biometrics for user authentication. The application of behavioral-based biometric authentication basically contains three major modules, namely, data capture, feature extraction, and classifier. This application is focusing on extracting the behavioral features related to the user and using these features for authentication measure. The objective is to determine the classifier techniques that mostly are used for data analysis during authentication process. From the comparison, we anticipate to discover the gap for improving the performance of behavioral-based biometric authentication. Additionally, we highlight the set of classifier techniques that are best performing for behavioral-based biometric authentication. IntechOpen United Kingdom IntechOpen 43-59 Recent Advances in Cryptography and Network Security
spellingShingle A Survey of Machine Learning Techniques for Behavioral-Based Biometric User Authentication
summary Authentication is a way to enable an individual to be uniquely identified usually based on passwords and personal identification number (PIN). The main problems of such authentication techniques are the unwillingness of the users to remember long and challenging combinations of numbers, letters, and symbols that can be lost, forged, stolen, or forgotten. In this paper, we investigate the current advances in the use of behavioral-based biometrics for user authentication. The application of behavioral-based biometric authentication basically contains three major modules, namely, data capture, feature extraction, and classifier. This application is focusing on extracting the behavioral features related to the user and using these features for authentication measure. The objective is to determine the classifier techniques that mostly are used for data analysis during authentication process. From the comparison, we anticipate to discover the gap for improving the performance of behavioral-based biometric authentication. Additionally, we highlight the set of classifier techniques that are best performing for behavioral-based biometric authentication.
title A Survey of Machine Learning Techniques for Behavioral-Based Biometric User Authentication
title_full A Survey of Machine Learning Techniques for Behavioral-Based Biometric User Authentication
title_fullStr A Survey of Machine Learning Techniques for Behavioral-Based Biometric User Authentication
title_full_unstemmed A Survey of Machine Learning Techniques for Behavioral-Based Biometric User Authentication
title_short A Survey of Machine Learning Techniques for Behavioral-Based Biometric User Authentication
title_sort survey of machine learning techniques for behavioral-based biometric user authentication