Keystroke Dynamics

Online learning has entirely transformed the way of learning by the students. Online tests and quizzes play an important role in online learning, which provides accurate results to the instructor. But, the learners use different methods to cheat during online exams such as opening a browser to search for the answer or a document in the local drive, etc. They are not authenticated once they login and progress to attend the online exams. Different techniques are used in authenticating the students taking up the online exams such as audio or video surveillance systems, fingerprint, or iris recognition, etc. Keystroke dynamics-based authentication (KDA) method, a behavioral biometric-based authentication model has gained focus in authenticating the users. This chapter proposes the usage of KDA as a solution to user authentication in online exams and presents a detailed review on the processes of KDA, the factors that affect the performance of KDA, their applications in different domains, and a few keystroke dynamics-based datasets to authenticate the users during online exams.

Biometric Authentication Techniques and Its Future

The number of users using the internet has drastically increased. Due to the large number of online users, demand has increased in various fields like social networks, knowledge sharing, commerce, etc. to protect the user's private data as well as control access. Unfortunately, the need for security and authentication for individual data also increased. In an attempt to confront the new risks unveiled by the networking revolution over the recent years, we need an efficient means for automatically recognizing the identity of individuals. Biometric authentication provides an improved level of security and paves the way to the future. Further, biometric authentication systems are classified as physiological biometric and behavioral biometric technologies. Further, the author provides ideas on research challenges and the future of authentication systems.

A Framework for Improving the Accuracy of Keystroke Dynamics-Based Biometric Authentication Using Soft Computing Techniques

The global access of information and resources from anywhere has increased the chance of intrusion and hacking of confidential data. Username with password is the commonly used authentication mechanism which is used for almost all online applications from net banking to online examinations. However, advanced safeguard mechanisms are sought against unauthorized access as the passwords can be hacked easily. To strengthen the password, it can be combined with biometric technology. Keystroke biometrics, a strong behavioral biometric, can be considered as a secure method compared to other methods even if the imposter knows the username and password as it is based on user habitual typing rhythm patterns. For any online application the accuracy level plays a vital role. But the accuracy of keystroke authentication when compared with other biometric authentication mechanisms is low. To improve the accuracy and minimize the training and testing time, this chapter proposes a wrapper-based classification using PSO-ELM-ANP algorithm which gives 99.92% accuracy.

Voice Biometric for Learner Authentication

The objective of this chapter is to verify the identity of the claimed learner by extracting the prosodic features of the speech signal. TIMIT Acoustic-Phonetic Continuous Speech Corpus is used for learner verification using prosodic and articulation features such as energy, pitch, and formants. The prosodic feature includes pitch (F0), and articulation feature includes formants (F1-F7). From this database, for this project in the training phase, 200 learners were used and in the testing phase 160 learners were used. The pitch and formants were extracted using linear predictive analysis. The first seven formants were used for verification purpose. The feature set consists of eight features. The features are fed into the Guassian mixture model. In the Gaussian mixture model, parameters are estimated from the training and testing data using the iterative expectation-maximization. Log likelihood score is computed using these parameters, and then these scores are normalized to make decisions. The decision is made based on the threshold.

Mitigation of Cheating in Online Exams

E-exams have different cheating opportunities and mitigations than paper exams, and remote exams also have different cheating risks that on-site exams. It is important to understand these differences in risk and possible mitigations against them. Authenticating the candidate may be a bigger challenge for remote exams, and biometric authentication has emerged as a key solution. This chapter delivers a categorization of different types of high-stakes assessments, different ways of cheating, and what types of cheating are most relevant for what types of assessments. It further presents an analysis of which threats biometric authentication can be effective against and what types of threats biometric authentication is less effective against. Insecure aspects of various biometric authentication approaches also indicate that biometric authentication and surveillance should be combined with other types of approaches (e.g., how questions are asked, timing of the exam) to mitigate cheating.

Keystroke Dynamics in E-Learning

Enhanced authentication is the need of the hour in today's technology. Commonly used login and password are not enough as they may be guessed by imposters. Most of the websites adopt the traditional authentication as login and password. But they don't verify whether the same person is accessing their information continuously in the current session. This is of great concern in distance-based e-learning systems. The institutes offering the e-courses must verify whether it is the same student who enrolled, is accessing their materials, doing the assignments themselves, and completing the examination without any cheating. In this case, one of the techniques, behavioral biometrics-keystroke dynamics, plays a very important role. Along with other authentication methods, keystroke dynamics can be combined to provide a more secured system for the students in e-learning environments. In this chapter, the basics of keystroke dynamics and some of the applications that use them are discussed.

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

This chapter explores a novel learning content management system. This chapter presents a novel system based on integration of voice authentication, face recognition technique, and signature of a person to recognize in e-learning system. Voice-based authentication, face recognition, and signature of a person is most widely used to authenticate human identity. The main concern in an e-learning system is to demotivate unknown users from taking the examination in place of the learner. Different techniques have been introduced to stop this fraud if any unknown person wants to imitate person's identity. In order to avoid the fraudulent handling of e-learning systems, the authentication based on voice recognition is discussed as one of the efficient techniques in literature.

Biometric Authentication Techniques in Online Learning Environments

The deployment of online e-learning can lead to many security risks, such as confidentiality loss, exposure of critical data, availability and destruction of publicly available information services. Security and proper authentication is critical in any online learning environment because any flaws can affect perceptions of its trustworthiness. Biometric authentication is increasingly being used in the newer generation of online learning environments for authentication of remote learners. Biometrics scan unique physiological characteristics in humans to identify people. These include fingerprints, iris, retina, voice, face, gait, and odor. The authors look at the state of biometric authentication techniques applicable to online learning environments and provide a more in-depth examination of face- and iris-based authentication systems for proper identification of learners.

Biometric Authentication Techniques and E-Learning

The primary goal of this chapter is to answer online exam frameworks by utilizing face acknowledgment to verify students for going to an online exam. A strategy in light of the utilization of neural systems to validate individuals' computerized unique mark framework for e-learning is present. This chapter centers around breaking down and contrasting the distinctive facial verification frameworks to confirm the understudies when they utilize e-learning stages, itemizing the expenses and the highlights of each structure recorded. Biometrics is a sensible verification used as a type of distinguishing proof and access control. It is additionally used to distinguish people in bunches that are under observation. Biometric identifiers are then particular quantifiable qualities used to mark and portray people. Biometric authenticators are as often as possible named as conduct and additionally physiological attributes. Physiological qualities are identified with the state of the body. In this chapter, the essential focus is on the distinctive biometrics and their applications.

Applications of Keystroke Dynamics Biometrics in Online Learning Environments

The biometric authentication in the online learning environment (OLE) is still exploratory and, despite an increase in keystroke dynamics biometrics research, many challenges remain in designing this authentication system due to the fact that it is economical and easily integrated with existing computer security system in OLE. Existing research in keystroke dynamics tends to focus on finding how keystroke dynamics of users can support non-intrusive authentications of users in OLEs. However, there is little evidence that researchers have approached the issue of unauthenticated users to take the role of authenticated users and perform tasks in the OLE with the intent of building models based on the keystroke dynamics of users. In a nutshell, the aim of this chapter is to provide an overview of the existing applications of keystroke dynamics as biometric authentication in the OLE, keystroke dynamics framework being designed for the OLE, advantages and disadvantages of a keystroke dynamics biometrics approach, as well as offering suggestions and possible future research directions.