A Smartwatch-Based Continuous Authentication System
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Authors
Gholami, Arash
Date
2024-05-16
Type
thesis
Language
eng
Keyword
Continuous Authentication , Authentication , Smartwatch-Based , Smartwatch , Hand-movement patterns , Handmovement , Handmovement Patterns , CNN , IF , OCSVM , KNN , LOF , Convolutional Neural Network , Siamese , Siamese CNN , Siamese Convolutional Neural Network , Learnt features , Learned features , Deep features , access control , User authentication , Equal error rate , CNN architectures , EER , Inertial sensor , Accelerometer , Gyroscope , one-class classifier
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Abstract
Conventional authentication methods can protect unattended devices if they are logged-out; however, an abandoned logged-in device remains vulnerable to unauthorized access. Inactivity timeouts can help to mitigate this threat; however, a long timeout increases susceptibility to attack, whereas a short timeout sacrifices usability. Continuous authentication aims to continuously and non-intrusively check if the user currently using the system is the same user who initially logged-in. If so, the user remains logged-in; otherwise, the user is logged-out.
We design and evaluate a comprehensive data processing pipeline for smartwatch-based continuous authentication systems using inertial sensor data. Our pipeline uses a Siamese convolutional neural network to learn and extract features and one-class classifiers to classify authentication attempts as either legitimate or malicious. To the best of our knowledge, our work is the first to use learned features and one-class classifiers for continuous authentication with smartwatch inertial sensor data. We compare our learned features with hand-picked features proposed in prior work; we show that our learned features achieve lower equal-error rate (EER) for shorter-duration time windows and achieve similar EER for longer-duration time windows. These results indicate that learned features are a promising approach to detect malicious authentication attempts more accurately in a shorter time window. Based on the insights gained from our work, we make recommendations for future work that would help improve the performance and real-world feasibility of smartwatch-based continuous authentication systems.
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Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution-NonCommercial 4.0 International
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution-NonCommercial 4.0 International