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

Abstract

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.

Optimized LOADng Routing Protocol Parameters Using Black Widow Optimization Algorithm for IoT

“…The system has a continuous user identification using multimodal biometrics to monitor the examiner to ensure that only a valid student takes the exam; a security layer that uses an eye tracker to watch/record student eye movement; and speech recognition to detect unwanted contact. (Mahadi et al, 2018) discussed several techniques and suggested combining (facial recognition and keystroke dynamics) could be the best classifiers in the online course for behavioral biometric authentication. Similarly, (Ghizlane et al, 2019) also suggested a combination of smart cards (to check student's identity) and face recognition techniques (for continuous monitoring of a student's webcam) to detect any suspicious behavior during the online exam and avoid any kinds of cheating attempts.…”

HOrBAC Optimization Based on Suspicious Behavior Detection Using Information Theory

“…The work [17] reviews ongoing authentication methods based on behavioural biometrics. The objective is to determine the classifier techniques that mostly are used for data analysis during authentication process.…”

Authentication and Authorization for Mobile IoT Devices Using Biofeatures: Recent Advances and Future Trends

“…Yampolskiy and Govindaraju [6] presented a a comprehensive study on behavioral biometrics. Mahadi et al [15] surveyed behavioralbased biometric user authentication, and determined the set of best classifiers for behavioral-based biometric authentication. Sundararajan and Woodard [16] surveyed different 100 approaches that leveraged deep learning and various biometric modalities to identify users.…”

“…This amount is calculated without taking into account the economic problems and psychological oppression that victims of this fraud suffer. From the banking sector and businesses, to access to homes, cars, personal computers and mobile devices, biometric technology offers the highest level of security in Gafurov (2007) [4] Not mobile Gait recognition No No No Revett et al (2008) [5] Not mobile Mouse dynamics No No No Yampolskiy and Govindaraju (2008) [6] Not mobile Behavioral-based No No No Shanmugapriya and Padmavathi (2009) [7] Not mobile Keystroke dynamics No No Yes Karnan et al (2011) [8] Not mobile Keystroke dynamics No No Yes Banerjee and Woodard (2012) [9] Not mobile Keystroke dynamics No No Yes Teh et al (2013) [10] Not mobile Keystroke dynamics No No Yes Bhatt et al (2013) [11] Not mobile Keystroke dynamics No No Yes Meng et al (2015) [12] Mobile device All Yes Yes Partial Teh et al (2016) [13] Mobile device Touch dynamics No No Yes Mahfouz et al (2017) [14] Smartphone behavioral-based No No Yes Mahadi et al (2018) [15] Not mobile behavioral-based No No Yes Sundararajan and Woodard (2018) [16] Not mobile All No No Yes [17] Mobile device Face recognition Yes Yes Yes Our survey Mobile IoT device All Yes Yes Yes ML and DM: Machine learning (ML) and data mining (DM) algorithms terms of privacy and privacy protection and secure access.…”