A Multimodal Behavioral Biometric Technique for User Identification using Mouse and Keystroke Dynamics

Motwani, Anand; Jain, Raina K.; Sondhi, Jyoti

doi:10.5120/19558-1307

Cited by 8 publications

(6 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate the accuracy of the proposed multimodal behavioral biometric system based on touchscreen Swipe and keystroke dynamics [23][24][25][26][27][28][29][30][31][32], we performed the following task in our experiments to train with binary classifiers such as Isolation Forest, k-NN, SVM, and Fuzzy with SVM Classifier. First, we divided the subjects into two parts: one was treated as the genuine subject and the other as the imposter subject.…”

Section: Evaluation Methodologymentioning

confidence: 99%

Multimodal Behavioral Biometric Authentication in Smartphones for Covid-19 Pandemic

Thapliyal

Verma

Kumar

2022

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

The usage of mobile phones has increased multi-fold in recent decades, mostly because of their utility in most aspects of daily life, such as communications, entertainment, and financial transactions. In use cases where users’ information is at risk from imposter attacks, biometrics-based authentication systems such as fingerprint or facial recognition are considered the most trustworthy in comparison to PIN, password, or pattern-based authentication systems in smartphones. Biometrics need to be presented at the time of power-on, they cannot be guessed or attacked through brute force and eliminate the possibility of shoulder surfing. However, fingerprints or facial recognition-based systems in smartphones may not be applicable in a pandemic situation like Covid-19, where hand gloves or face masks are mandatory to protect against unwanted exposure of the body parts. This paper investigates the situations in which fingerprints cannot be utilized due to hand gloves and hence presents an alternative biometric system using the multimodal Touchscreen swipe and Keystroke dynamics pattern. We propose a HandGlove mode of authentication where the system will automatically be triggered to authenticate a user based on Touchscreen swipe and Keystroke dynamics patterns. Our experimental results suggest that the proposed multimodal biometric system can operate with high accuracy. We experiment with different classifiers like Isolation Forest Classifier, SVM, k-NN Classifier, and fuzzy logic classifier with SVM to obtain the best authentication accuracy of 99.55% with 197 users on the Samsung Galaxy S20. We further study the problem of untrained external factors which can impact the user experience of authentication system and propose a model based on fuzzy logic to extend the functionality of the system to improve under novel external effects. In this experiment, we considered the untrained external factor of ‘sanitized hands’ with which the user tries to authenticate and achieved 93.5% accuracy in this scenario. The proposed multimodal system could be one of the most sought approaches for biometrics-based authentication in smartphones in a COVID-19 pandemic situation.

show abstract

Section: Evaluation Methodologymentioning

confidence: 99%

Multimodal Behavioral Biometric Authentication in Smartphones for Covid-19 Pandemic

Thapliyal

Verma

Kumar

2022

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

show abstract

“…Motwani et al [14] in their work, the dataset was dynamically generated and the impostors were not involved during the enrollment phase, false rejection rate (FRR) was 3.2% with only 27 features. Stanciu et al [15] focused on effectiveness of sensor-enhanced keystroke dynamics, they have utilized movement sensors that is accelerometer and gyroscope in their work.…”

Section: Issn: 2088-8708 mentioning

confidence: 99%

Behavioral biometric based personal authentication in feature phones

Thapliyal

Verma

Kumar

2022

IJECE

View full text Add to dashboard Cite

<p><span>The usage of mobile phones has increased multifold in the recent decades mostly because of its utility in most of the aspects of daily life, such as communications, entertainment, and financial transactions. Feature phones are generally the keyboard based or lower version of touch based mobile phones, mostly targeted for efficient calling and messaging. In comparison to smart phones, feature phones have no provision of a biometrics system for the user access. The literature, have shown very less attempts in designing a biometrics system which could be most suitable to the low-cost feature phones. A biometric system utilizes the features and attributes based on the physiological or behavioral properties of the individual. In this research, we explore the usefulness of keystroke dynamics for feature phones which offers an efficient and versatile biometric framework. In our research, we have suggested an approach to incorporate the user’s typing patterns to enhance the security in the feature phone. We have applied k-nearest neighbors (k-NN) with fuzzy logic and achieved the equal error rate (EER) 1.88% to get the better accuracy. The experiments are performed with 25 users on Samsung On7 Pro C3590. On comparison, our proposed technique is competitive with almost all the other techniques available in the literature.</span></p>

show abstract

“…Motwani et al [19,20] propose a multi-modal behavioural biometric technique to authenticate/identify users by the way they interact with input devices, namely the mouse and keyboard. Attaullah et al [8] has proposed a new bimodal behavioural biometric solution for user authentication.…”

Section: Related Work On Security Techniques Based On Behavioural Bimentioning

confidence: 99%

HOrBAC Optimization Based on Suspicious Behavior Detection Using Information Theory

Quimatio

FIDELE

2021

SN COMPUT. SCI.

View full text Add to dashboard Cite

Several access control models have been developed: discretionary, mandatory, role-based, organisation-based or the recent hierarchical organisation-based. The hierarchical organisation-based model is best suited to counter internal attacks and fraud because it integrates a digital parafer that performs proactive hierarchical control of all activities carried out by the organisation's employees in the system, including those of the super-user. However, because this control is based solely on the type of requests issued by internal employees, this model is vulnerable to Denial of Service attacks in a Big Data environment. The purpose of this article is to optimise the number of requests awaiting hierarchical treatment in the hierarchical organisation-based access control model while maintaining a maximum level of security. For this purpose, we have proposed an additional layer of control of the digital parafer based on suspicious behaviour detection from the entropy measurement. Finally, we carried out a mathematical and experimental comparisons between the two approaches in terms of number of requests awaiting treatment in the system and obtained a satisfactory result.

show abstract

A Multimodal Behavioral Biometric Technique for User Identification using Mouse and Keystroke Dynamics

Cited by 8 publications

References 8 publications

Multimodal Behavioral Biometric Authentication in Smartphones for Covid-19 Pandemic

Multimodal Behavioral Biometric Authentication in Smartphones for Covid-19 Pandemic

Behavioral biometric based personal authentication in feature phones

HOrBAC Optimization Based on Suspicious Behavior Detection Using Information Theory

Contact Info

Product

Resources

About