Identifying User Authentication and Most Frequently Used Region Based on Mouse Movement Data: A Machine Learning Approach

Rahman, Md. Muhaimenur; Basak, Sarnali

doi:10.1109/ccwc51732.2021.9376087

Cited by 7 publications

(1 citation statement)

References 21 publications

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“…The authors of [30] described Y-eyes, a challenge-response BAS that uses lightweight machine learning approaches to construct numerous screen picture patterns as a challenge and detect relevant corneal specular reflection responses from human eyes. In [31], the authors presented a BAS based on detecting the most frequently used region in five-and ten-second data of mouse movements. The authors of [32] proposed a BAS that employs facial recognition as well as the unique gestures of that particular face when pronouncing a password.…”

Section: Literature Reviewmentioning

confidence: 99%

Challenge-Response Emotion Authentication Algorithm Using Modified Horizontal Deep Learning

Ezz¹,

Mostafa²,

Elshenawy³

2023

Intelligent Automation &Amp; Soft Computing

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Face authentication is an important biometric authentication method commonly used in security applications. It is vulnerable to different types of attacks that use authorized users' facial images and videos captured from social media to perform spoofing attacks and dynamic movements for penetrating security applications. This paper presents an innovative challenge-response emotions authentication model based on the horizontal ensemble technique. The proposed model provides high accurate face authentication process by challenging the authorized user using a random sequence of emotions to provide a specific response for every authentication trial with a different sequence of emotions. The proposed model is applied to the KDEF dataset using 10-fold cross-validations. Several improvements are made to the proposed model. First, the VGG16 model is applied to the seven common emotions. Second, the system usability is enhanced by analyzing and selecting only the four common and easy-to-use emotions. Third, the horizontal ensemble technique is applied to enhance the emotion recognition accuracy and minimize the error during authentication processes. Finally, the Horizontal Ensemble Best N-Losses (HEBNL) is applied using challenge-response emotion to improve the authentication efficiency and minimize the computational power. The successive improvements implemented on the proposed model led to an improvement in the accuracy from 92.1% to 99.27%.

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Section: Literature Reviewmentioning

confidence: 99%

Challenge-Response Emotion Authentication Algorithm Using Modified Horizontal Deep Learning

Ezz¹,

Mostafa²,

Elshenawy³

2023

Intelligent Automation &Amp; Soft Computing

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An Efficient Deep Learning Approach for Detecting Pneumonia Using the Convolutional Neural Network

Saha

Rahman

2021

Advances in Intelligent Systems and Computing

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User identification and authentication in browser environments via machine learning

Uymin

2024

E3S Web of Conf.

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Achieving secure and efficient user identification on computer systems necessitates the deployment of strong protective mechanisms, given that conventional password approaches are insufficient to counter significant security threats. Behavioral biometric technologies have been developed to address these security challenges. This study focuses on user authentication via mouse movement dynamics, proposing a novel biometric approach for network administrators who exhibit unique mouse movement patterns. The method leverages mouse movement data over five and ten-second intervals, using features extracted from these data to identify frequent usage areas. Five machine learning algorithms were evaluated, with the Random Forest algorithm demonstrating superior performance. The method achieves a FPR of 0.85% and a FNR of 29.17%, underscoring its potential for enhancing security in network administration tasks. The dataset was generated from mouse movement during training sessions and various competitions, and features were extracted and classified to evaluate the system’s accuracy. The study concludes that Random Forest is the most effective algorithm for this application, meeting regional biometric system standards and suggesting potential for widespread implementation in corporate environments.

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Identifying User Authentication and Most Frequently Used Region Based on Mouse Movement Data: A Machine Learning Approach

Cited by 7 publications

References 21 publications

Challenge-Response Emotion Authentication Algorithm Using Modified Horizontal Deep Learning

Challenge-Response Emotion Authentication Algorithm Using Modified Horizontal Deep Learning

An Efficient Deep Learning Approach for Detecting Pneumonia Using the Convolutional Neural Network

User identification and authentication in browser environments via machine learning

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