Siamese Neural Network for Keystroke Dynamics-Based Authentication on Partial Passwords
The paper addresses issues concerning secure authentication in computer systems. We focus on multi-factor authentication methods using two or more independent mechanisms to identify a user. User-specific behavioral biometrics is widely used to increase login security. The usage of behavioral biometr...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/15/6685 |
| _version_ | 1827730890549100544 |
|---|---|
| author | Kamila Lis Ewa Niewiadomska-Szynkiewicz Katarzyna Dziewulska |
| author_facet | Kamila Lis Ewa Niewiadomska-Szynkiewicz Katarzyna Dziewulska |
| author_sort | Kamila Lis |
| collection | DOAJ |
| description | The paper addresses issues concerning secure authentication in computer systems. We focus on multi-factor authentication methods using two or more independent mechanisms to identify a user. User-specific behavioral biometrics is widely used to increase login security. The usage of behavioral biometrics can support verification without bothering the user with a requirement of an additional interaction. Our research aimed to check whether using information about how partial passwords are typed is possible to strengthen user authentication security. The partial password is a query of a subset of characters from a full password. The use of partial passwords makes it difficult for attackers who can observe password entry to acquire sensitive information. In this paper, we use a Siamese neural network and n-shot classification using past recent logins to verify user identity based on keystroke dynamics obtained from the static text. The experimental results on real data demonstrate that keystroke dynamics authentication can be successfully used for partial password typing patterns. Our method can support the basic authentication process and increase users’ confidence. |
| first_indexed | 2024-03-11T00:17:55Z |
| format | Article |
| id | doaj.art-ebe217952f1544ed8e30075db7f2cf8d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:17:55Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ebe217952f1544ed8e30075db7f2cf8d2023-11-18T23:33:04ZengMDPI AGSensors1424-82202023-07-012315668510.3390/s23156685Siamese Neural Network for Keystroke Dynamics-Based Authentication on Partial PasswordsKamila Lis0Ewa Niewiadomska-Szynkiewicz1Katarzyna Dziewulska2Research and Academic Computer Network, Kolska 12, 01-045 Warsaw, PolandInstitute of Control and Computation Engineering, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, PolandResearch and Academic Computer Network, Kolska 12, 01-045 Warsaw, PolandThe paper addresses issues concerning secure authentication in computer systems. We focus on multi-factor authentication methods using two or more independent mechanisms to identify a user. User-specific behavioral biometrics is widely used to increase login security. The usage of behavioral biometrics can support verification without bothering the user with a requirement of an additional interaction. Our research aimed to check whether using information about how partial passwords are typed is possible to strengthen user authentication security. The partial password is a query of a subset of characters from a full password. The use of partial passwords makes it difficult for attackers who can observe password entry to acquire sensitive information. In this paper, we use a Siamese neural network and n-shot classification using past recent logins to verify user identity based on keystroke dynamics obtained from the static text. The experimental results on real data demonstrate that keystroke dynamics authentication can be successfully used for partial password typing patterns. Our method can support the basic authentication process and increase users’ confidence.https://www.mdpi.com/1424-8220/23/15/6685keystroke dynamicsbehavioral biometrykeyboardpartial password authenticationsiamese network |
| spellingShingle | Kamila Lis Ewa Niewiadomska-Szynkiewicz Katarzyna Dziewulska Siamese Neural Network for Keystroke Dynamics-Based Authentication on Partial Passwords Sensors keystroke dynamics behavioral biometry keyboard partial password authentication siamese network |
| title | Siamese Neural Network for Keystroke Dynamics-Based Authentication on Partial Passwords |
| title_full | Siamese Neural Network for Keystroke Dynamics-Based Authentication on Partial Passwords |
| title_fullStr | Siamese Neural Network for Keystroke Dynamics-Based Authentication on Partial Passwords |
| title_full_unstemmed | Siamese Neural Network for Keystroke Dynamics-Based Authentication on Partial Passwords |
| title_short | Siamese Neural Network for Keystroke Dynamics-Based Authentication on Partial Passwords |
| title_sort | siamese neural network for keystroke dynamics based authentication on partial passwords |
| topic | keystroke dynamics behavioral biometry keyboard partial password authentication siamese network |
| url | https://www.mdpi.com/1424-8220/23/15/6685 |
| work_keys_str_mv | AT kamilalis siameseneuralnetworkforkeystrokedynamicsbasedauthenticationonpartialpasswords AT ewaniewiadomskaszynkiewicz siameseneuralnetworkforkeystrokedynamicsbasedauthenticationonpartialpasswords AT katarzynadziewulska siameseneuralnetworkforkeystrokedynamicsbasedauthenticationonpartialpasswords |