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...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Kamila Lis [verfasserIn] Ewa Niewiadomska-Szynkiewicz [verfasserIn] Katarzyna Dziewulska [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	keystroke dynamics behavioral biometry keyboard partial password authentication siamese network

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 23(2023), 15, p 6685
Übergeordnetes Werk:	volume:23 ; year:2023 ; number:15, p 6685

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s23156685

Katalog-ID:	DOAJ093679351

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allfieldsSound	10.3390/s23156685 doi (DE-627)DOAJ093679351 (DE-599)DOAJebe217952f1544ed8e30075db7f2cf8d DE-627 ger DE-627 rakwb eng TP1-1185 Kamila Lis verfasserin aut Siamese Neural Network for Keystroke Dynamics-Based Authentication on Partial Passwords 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. keystroke dynamics behavioral biometry keyboard partial password authentication siamese network Chemical technology Ewa Niewiadomska-Szynkiewicz verfasserin aut Katarzyna Dziewulska verfasserin aut In Sensors MDPI AG, 2003 23(2023), 15, p 6685 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:15, p 6685 https://doi.org/10.3390/s23156685 kostenfrei https://doaj.org/article/ebe217952f1544ed8e30075db7f2cf8d kostenfrei https://www.mdpi.com/1424-8220/23/15/6685 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 15, p 6685
language	English
source	In Sensors 23(2023), 15, p 6685 volume:23 year:2023 number:15, p 6685
sourceStr	In Sensors 23(2023), 15, p 6685 volume:23 year:2023 number:15, p 6685
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	keystroke dynamics behavioral biometry keyboard partial password authentication siamese network Chemical technology
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container_title	Sensors
authorswithroles_txt_mv	Kamila Lis @@aut@@ Ewa Niewiadomska-Szynkiewicz @@aut@@ Katarzyna Dziewulska @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
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callnumber-first	T - Technology
author	Kamila Lis
spellingShingle	Kamila Lis misc TP1-1185 misc keystroke dynamics misc behavioral biometry misc keyboard misc partial password authentication misc siamese network misc Chemical technology Siamese Neural Network for Keystroke Dynamics-Based Authentication on Partial Passwords
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topic_title	TP1-1185 Siamese Neural Network for Keystroke Dynamics-Based Authentication on Partial Passwords keystroke dynamics behavioral biometry keyboard partial password authentication siamese network
topic	misc TP1-1185 misc keystroke dynamics misc behavioral biometry misc keyboard misc partial password authentication misc siamese network misc Chemical technology
topic_unstemmed	misc TP1-1185 misc keystroke dynamics misc behavioral biometry misc keyboard misc partial password authentication misc siamese network misc Chemical technology
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title	Siamese Neural Network for Keystroke Dynamics-Based Authentication on Partial Passwords
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title_auth	Siamese Neural Network for Keystroke Dynamics-Based Authentication on Partial Passwords
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Siamese Neural Network for Keystroke Dynamics-Based Authentication on Partial Passwords
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