Dynamic Keystroke Technique for a Secure Authentication System based on Deep Belief Nets
The rapid growth of electronic assessment in various fields has led to the emergence of issues such as user identity fraud and cheating. One potential solution to these problems is to use a complementary authentication method, such as a behavioral biometric characteristic that is unique to each ind...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
D. G. Pylarinos
2023-06-01
|
| Series: | Engineering, Technology & Applied Science Research |
| Subjects: | |
| Online Access: | https://etasr.com/index.php/ETASR/article/view/5841 |
| _version_ | 1797700226266431488 |
|---|---|
| author | Asia Othman Aljahdali Fursan Thabit Hanan Aldissi Wafaa Nagro |
| author_facet | Asia Othman Aljahdali Fursan Thabit Hanan Aldissi Wafaa Nagro |
| author_sort | Asia Othman Aljahdali |
| collection | DOAJ |
| description |
The rapid growth of electronic assessment in various fields has led to the emergence of issues such as user identity fraud and cheating. One potential solution to these problems is to use a complementary authentication method, such as a behavioral biometric characteristic that is unique to each individual. One promising approach is keystroke dynamics, which involves analyzing the typing patterns of users. In this research, the Deep Belief Nets (DBN) model is used to implement a dynamic keystroke technique for secure e-assessment. The proposed system extracts various features from the pressure-time measurements, digraphs (dwell time and flight time), trigraphs, and n-graphs, and uses these features to classify the user's identity by applying the DBN algorithm to a dataset collected from participants who typed free text using a standard QWERTY keyboard in a neutral state without inducing specific emotions. The DBN model is designed to detect cheating attempts and is tested on a dataset collected from the proposed e-assessment system using free text. The implementation of the DBN results in an error rate of 5% and an accuracy of 95%, indicating that the system is effective in identifying users' identities and cheating, providing a secure e-assessment approach.
|
| first_indexed | 2024-03-12T04:19:14Z |
| format | Article |
| id | doaj.art-ac9d3b0ab1c84f0a909cdac9224a65ab |
| institution | Directory Open Access Journal |
| issn | 2241-4487 1792-8036 |
| language | English |
| last_indexed | 2024-03-12T04:19:14Z |
| publishDate | 2023-06-01 |
| publisher | D. G. Pylarinos |
| record_format | Article |
| series | Engineering, Technology & Applied Science Research |
| spelling | doaj.art-ac9d3b0ab1c84f0a909cdac9224a65ab2023-09-03T10:34:25ZengD. G. PylarinosEngineering, Technology & Applied Science Research2241-44871792-80362023-06-0113310.48084/etasr.5841Dynamic Keystroke Technique for a Secure Authentication System based on Deep Belief NetsAsia Othman Aljahdali0Fursan Thabit1Hanan Aldissi2Wafaa Nagro 3College of Computer Science and Engineering, University of Jeddah, Saudi ArabiaDepartment of Computer Engineering, Faculty of Engineering, Ege University, TurkeyCollege of Computer Science and Engineering, University of Jeddah, Saudi ArabiaCollege of Computer Science and Engineering, University of Jeddah, Saudi Arabia The rapid growth of electronic assessment in various fields has led to the emergence of issues such as user identity fraud and cheating. One potential solution to these problems is to use a complementary authentication method, such as a behavioral biometric characteristic that is unique to each individual. One promising approach is keystroke dynamics, which involves analyzing the typing patterns of users. In this research, the Deep Belief Nets (DBN) model is used to implement a dynamic keystroke technique for secure e-assessment. The proposed system extracts various features from the pressure-time measurements, digraphs (dwell time and flight time), trigraphs, and n-graphs, and uses these features to classify the user's identity by applying the DBN algorithm to a dataset collected from participants who typed free text using a standard QWERTY keyboard in a neutral state without inducing specific emotions. The DBN model is designed to detect cheating attempts and is tested on a dataset collected from the proposed e-assessment system using free text. The implementation of the DBN results in an error rate of 5% and an accuracy of 95%, indicating that the system is effective in identifying users' identities and cheating, providing a secure e-assessment approach. https://etasr.com/index.php/ETASR/article/view/5841keystroke dynamicsauthenticatione-assessmentdwell timeflight timeDeep Belief Network (DBN) |
| spellingShingle | Asia Othman Aljahdali Fursan Thabit Hanan Aldissi Wafaa Nagro Dynamic Keystroke Technique for a Secure Authentication System based on Deep Belief Nets Engineering, Technology & Applied Science Research keystroke dynamics authentication e-assessment dwell time flight time Deep Belief Network (DBN) |
| title | Dynamic Keystroke Technique for a Secure Authentication System based on Deep Belief Nets |
| title_full | Dynamic Keystroke Technique for a Secure Authentication System based on Deep Belief Nets |
| title_fullStr | Dynamic Keystroke Technique for a Secure Authentication System based on Deep Belief Nets |
| title_full_unstemmed | Dynamic Keystroke Technique for a Secure Authentication System based on Deep Belief Nets |
| title_short | Dynamic Keystroke Technique for a Secure Authentication System based on Deep Belief Nets |
| title_sort | dynamic keystroke technique for a secure authentication system based on deep belief nets |
| topic | keystroke dynamics authentication e-assessment dwell time flight time Deep Belief Network (DBN) |
| url | https://etasr.com/index.php/ETASR/article/view/5841 |
| work_keys_str_mv | AT asiaothmanaljahdali dynamickeystroketechniqueforasecureauthenticationsystembasedondeepbeliefnets AT fursanthabit dynamickeystroketechniqueforasecureauthenticationsystembasedondeepbeliefnets AT hananaldissi dynamickeystroketechniqueforasecureauthenticationsystembasedondeepbeliefnets AT wafaanagro dynamickeystroketechniqueforasecureauthenticationsystembasedondeepbeliefnets |