Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication
Static authentication methods, like passwords, grow increasingly weak with advancements in technology and attack strategies. Continuous authentication has been proposed as a solution, in which users who have gained access to an account are still monitored in order to continuously verify that the use...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Machine Learning and Knowledge Extraction |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-4990/4/2/23 |
| _version_ | 1827659047187251200 |
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| author | Nyle Siddiqui Rushit Dave Mounika Vanamala Naeem Seliya |
| author_facet | Nyle Siddiqui Rushit Dave Mounika Vanamala Naeem Seliya |
| author_sort | Nyle Siddiqui |
| collection | DOAJ |
| description | Static authentication methods, like passwords, grow increasingly weak with advancements in technology and attack strategies. Continuous authentication has been proposed as a solution, in which users who have gained access to an account are still monitored in order to continuously verify that the user is not an imposter who had access to the user credentials. Mouse dynamics is the behavior of a user’s mouse movements and is a biometric that has shown great promise for continuous authentication schemes. This article builds upon our previous published work by evaluating our dataset of 40 users using three machine learning and three deep learning algorithms. Two evaluation scenarios are considered: binary classifiers are used for user authentication, with the top performer being a 1-dimensional convolutional neural network (1D-CNN) with a peak average test accuracy of 85.73% across the top-10 users. Multi-class classification is also examined using an artificial neural network (ANN) which reaches an astounding peak accuracy of 92.48%, the highest accuracy we have seen for any classifier on this dataset. |
| first_indexed | 2024-03-09T23:14:05Z |
| format | Article |
| id | doaj.art-d064a57e0c414b27b04f42cb93d55add |
| institution | Directory Open Access Journal |
| issn | 2504-4990 |
| language | English |
| last_indexed | 2024-03-09T23:14:05Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machine Learning and Knowledge Extraction |
| spelling | doaj.art-d064a57e0c414b27b04f42cb93d55add2023-11-23T17:40:28ZengMDPI AGMachine Learning and Knowledge Extraction2504-49902022-05-014250251810.3390/make4020023Machine and Deep Learning Applications to Mouse Dynamics for Continuous User AuthenticationNyle Siddiqui0Rushit Dave1Mounika Vanamala2Naeem Seliya3Department of Computer Science, University of Wisconsin—Eau Claire, Eau Claire, WI 54701, USADepartment of Computer Science, University of Wisconsin—Eau Claire, Eau Claire, WI 54701, USADepartment of Computer Science, University of Wisconsin—Eau Claire, Eau Claire, WI 54701, USADepartment of Computer Science, University of Wisconsin—Eau Claire, Eau Claire, WI 54701, USAStatic authentication methods, like passwords, grow increasingly weak with advancements in technology and attack strategies. Continuous authentication has been proposed as a solution, in which users who have gained access to an account are still monitored in order to continuously verify that the user is not an imposter who had access to the user credentials. Mouse dynamics is the behavior of a user’s mouse movements and is a biometric that has shown great promise for continuous authentication schemes. This article builds upon our previous published work by evaluating our dataset of 40 users using three machine learning and three deep learning algorithms. Two evaluation scenarios are considered: binary classifiers are used for user authentication, with the top performer being a 1-dimensional convolutional neural network (1D-CNN) with a peak average test accuracy of 85.73% across the top-10 users. Multi-class classification is also examined using an artificial neural network (ANN) which reaches an astounding peak accuracy of 92.48%, the highest accuracy we have seen for any classifier on this dataset.https://www.mdpi.com/2504-4990/4/2/23deep learningmachine learningmouse dynamicscontinuous user authenticationmulti-class classification |
| spellingShingle | Nyle Siddiqui Rushit Dave Mounika Vanamala Naeem Seliya Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication Machine Learning and Knowledge Extraction deep learning machine learning mouse dynamics continuous user authentication multi-class classification |
| title | Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication |
| title_full | Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication |
| title_fullStr | Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication |
| title_full_unstemmed | Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication |
| title_short | Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication |
| title_sort | machine and deep learning applications to mouse dynamics for continuous user authentication |
| topic | deep learning machine learning mouse dynamics continuous user authentication multi-class classification |
| url | https://www.mdpi.com/2504-4990/4/2/23 |
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