An Enhanced Integration of Voice-, Face-, and Signature-Based Authentication System for Learning Content Management System

Introduction

In recent years, lot of research is been carried out to enhance the security of E-learning system. However, still there is lot of scope to develop a strong learning content management system (LCMS), that can authenticate and allow the right user to access the E-learning content and also to create materials in E-learning platform. In reality, the exponential growth in usage of smart hand held devices; need focus on building new enhanced LCMS to meet the requirement of mobility and versatile wireless environments. Further, security system of such LCMS should support mechanisms like data confidentiality, user authentication, data authorization and also user accountability.

The conventional methods of authentication like user id and password has several disadvantages. To specify, the user may forget or misplace the user id /password. Also, the misuse of user id /password may lead to voluntary damage to the E- learning content and data. The best alternate solutions are to use human physiological characteristics and next human behaviour characteristics. Under the human physiological characteristic, the biometric process of user authentication is performed. These biometric techniques prove to be more reliable and advantageous over conventional authentication mechanism. The biometric authentication involves using (i) speech signals (i) finger print patterns (ii) iris pattern of a user. In finger print biometric authentication, unique fingerprint patterns in individual is used to recognition the exact user. Similarly, in iris pattern mechanism, the unique furrows and ridges of the user iris is used. These techniques of authentication have several advantages over conventional password/user id based authentication.

The human behaviour or characteristic based authentication system involves human brain activities and human eye movement activities. This system uses brain activity of user, measured through bio sensors attached to the users. Here, the brain waves are monitored using bio metric sensors. Then, the observed brain wave patterns are used for user authentication. Similarly, the eye movement patterns of users are observed and used as biometric sensor to authenticate the user.

The E-learning systems are designed in such a way that they able to mark the attendance of E-learners. The current multi model biometric systems perform identification, authentication and tracking of the user. Basically, this model involves behavioural biometric characteristics such as mouse movements, keystroke dynamics, El-Gayar, M. M., & Soliman, H., 2013; Anuradha.S.G, Kavya.B, Akshatha.S, Kothapalli Jyothi, Gudipati Ashalatha.,2016; Xiao, H., Ji, W., & Ullah, A.,2011) and physical characteristics such as face features (GUILLÉN-GÁMEZ, F. D.,2017; Beaudin, S.,2016; Obeidallah, R., Ahmad, A. A., Farouq, F., & Awad, S. 2015.; Penteado, B. E., & Marana, A. N. 2009).

In literature, different types of learning algorithms have been studied and the uses of these biometrics technologies have been demonstrated. Further, the experimental results of these studies have mainly focused the verification and attendance control processes. Also, it is observed that the existing solutions mostly need a lower level of students' collaboration. After the implementation of this model it can be added to the existing LMS and become compatible with it. Therefore, this model can be used to track the continuous attendance of the users in sensitive stages of E- learning. Next, the password authentication is one of the mechanisms that ensure the right student has login the web enabled learning courses. This mechanism has its own drawbacks such as if a learner do not want to attend a particular session or want to miss out any exam then learner can illegitimately share his login id or password to some other user. Overcome, this illegitimate sharing of passwords, there is need for biometric applications such as behavioural authentication or physical authentication mechanisms. Hence for secured authentication, popularization of low cost biometric enabled devices becomes essential. Physical authentication such as face recognition is another technique in which the image of a learner is captured online via webcam. Partial result shows success of confirmation of presence of user (Chutel, P. M., & Sakhare, A., 2014).