Mouse dynamics correlates to student behaviour in computer-based exams

Nowadays, it is common for higher education institutions to use computer-based exams, partly or integrally, in their evaluation processes. These exams, much like their paper-based counterparts, are one of the most significant sources of stress in the life of students. However, the fact that exams are undertaken in a computer allows for new features to be acquired that may provide more reliable insights into the behaviour and state of the student during the exam. In this article we analyse these novel behavioural features and explore, to which extent, they can point out previously unknown phenomena. Specifically, we show that the time a student takes to complete an exam is correlated with mouse dynamics features. In practical terms, we are able to predict the duration of each individual exam with a satisfying error based on the interaction patterns of the student.COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013. This work was funded by ‘EUSTRESS – Sistema de Informação para a monitorização e avaliação ̧ dos níveis do stress e previsão de stress cónico’, N◦2015/017832 P2020 SI I&DT, (NUP, NORTE-01-0247-FEDER-017832) in co-promotion between Optimizer-Lda and ICVS/3B’s-Uminhoinfo:eu-repo/semantics/publishedVersio

False Identity Detection Using Complex Sentences

The use of faked identities is a current issue for both physical and online security. In this paper, we test the differences between subjects who report their true identity and the ones who give fake identity responding to control, simple, and complex questions. Asking complex questions is a new procedure for increasing liars' cognitive load, which is presented in this paper for the first time. The experiment consisted in an identity verification task, during which response time and errors were collected. Twenty participants were instructed to lie about their identity, whereas the other 20 were asked to respond truthfully. Different machine learning (ML) models were trained, reaching an accuracy level around 90-95% in distinguishing liars from truth tellers based on error rate and response time. Then, to evaluate the generalization and replicability of these models, a new sample of 10 participants were tested and classified, obtaining an accuracy between 80 and 90%. In short, results indicate that liars may be efficiently distinguished from truth tellers on the basis of their response times and errors to complex questions, with an adequate generalization accuracy of the classification models

Implementation of Mouse Gesture Recognition

In this paper, we construct Authentication of automatic data processing system by Mouse Gestures was summarized and its significance towards its Methodologies was illustrated. Based on Neural Network formula and its analysis has been user to attain the Biometric Authentication based on user behavior on Neural Network and is additionally surveyed. Our This research paper conducts a review of the realm of Artificial Neural Network and biometric methods that add another more secure layer of security to computing system. DOI: 10.17762/ijritcc2321-8169.150519

User Identification and Authentication using Multi-Modal Behavioral Biometrics

Biometric computer authentication has an advantage over password and access card authentication in that it is based on something you are, which is not easily copied or stolen. One way of performing biometric computer authentication is to use behavioral tendencies associated with how a user interacts with the computer. However, behavioral biometric authentication accuracy rates are worse than more traditional authentication methods. This article presents a behavioral biometric system that fuses user data from keyboard, mouse, and Graphical User Interface (GUI) interactions. Combining the modalities results in a more accurate authentication decision based on a broader view of the user\u27s computer activity while requiring less user interaction to train the system than previous work. Testing over 31 users shows that fusion techniques significantly improve behavioral biometric authentication accuracy over single modalities on their own. Between the two fusion techniques presented, feature fusion and an ensemble based classification method, the ensemble method performs the best with a False Acceptance Rate (FAR) of 2.10% and a False Rejection Rate (FRR) 2.24%

User Identification based on Touch Dynamics

Touch interaction has quickly become the de-facto means of interacting with handheld devices due to its perceived attractiveness and low hardware cost. This study proposes a strategy for identifying users based on touch dynamics. Users' touch behavior is monitored and several unique features are extracted including left versus right hand dominance, one- handed versus bimanual operation, stroke size, stroke timing, symmetry, stroke speed and timing regularity. An experiment involving 20 users reveals that the strategy is successful in identifying users and their traits according to the touch dynamics. The results can be used for automatic user interface customization. However, more research is needed before touch characteristics can be applied to increasing the security of handheld touch-based devices

Integration of biometrics and steganography: A comprehensive review

The use of an individual’s biometric characteristics to advance authentication and verification technology beyond the current dependence on passwords has been the subject of extensive research for some time. Since such physical characteristics cannot be hidden from the public eye, the security of digitised biometric data becomes paramount to avoid the risk of substitution or replay attacks. Biometric systems have readily embraced cryptography to encrypt the data extracted from the scanning of anatomical features. Significant amounts of research have also gone into the integration of biometrics with steganography to add a layer to the defence-in-depth security model, and this has the potential to augment both access control parameters and the secure transmission of sensitive biometric data. However, despite these efforts, the amalgamation of biometric and steganographic methods has failed to transition from the research lab into real-world applications. In light of this review of both academic and industry literature, we suggest that future research should focus on identifying an acceptable level steganographic embedding for biometric applications, securing exchange of steganography keys, identifying and address legal implications, and developing industry standards

Android Based Behavioral Biometric Authentication via Multi-Modal Fusion

Because mobile devices are easily lost or stolen, continuous authentication is extremely desirable for them. Behavioral biometrics provides non-intrusive continuous authentication that has much less impact on usability than active authentication. However single-modality behavioral biometrics has proven less accurate than standard active authentication. This thesis presents a behavioral biometric system that uses multi-modal fusion with user data from touch, keyboard, and orientation sensors. Testing of ve users shows that fusion of modalities provides more accurate authentication than each individual modalities by itself. Using the BayesNet classification algorithm, fusion achieves False Acceptance Rate (FAR) and False Rejection Rate (FRR) values of 9.65% and 2% respectively, each of which is 8% lower than the closest individual modality

Computer Based Behavioral Biometric Authentication via Multi-Modal Fusion

Biometric computer authentication has an advantage over password and access card authentication in that it is based on something you are, which is not easily copied or stolen. One way of performing biometric computer authentication is to use behavioral tendencies associated with how a user interacts with the computer. However, behavioral biometric authentication accuracy rates are much larger then more traditional authentication methods. This thesis presents a behavioral biometric system that fuses user data from keyboard, mouse, and Graphical User Interface (GUI) interactions. Combining the modalities results in a more accurate authentication decision based on a broader view of the user\u27s computer activity while requiring less user interaction to train the system than previous work. Testing over 30 users, shows that fusion techniques significantly improve behavioral biometric authentication accuracy over single modalities on their own. Two fusion techniques are presented, feature fusion and decision level fusion. Using an ensemble based classification method the decision level fusion technique improves the FAR by 0.86% and FRR by 2.98% over the best individual modality