Toward supervised shape-based behavioral authentication on smartphones
Currently, smartphone security has received much more attention as users may use their devices to perform various sensitive tasks. For example, users can utilize mobile banking applications for online shopping, which may store many sensitive data on their devices. Hence there is a need to authentica...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Wenjuan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Models of agglomerate growth in fluidized bed reactors: Critical review, status and applications - Khadilkar, Aditi ELSEVIER, 2014, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:55 ; year:2020 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.jisa.2020.102591 |
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ELV052435431 |
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| 520 | |a Currently, smartphone security has received much more attention as users may use their devices to perform various sensitive tasks. For example, users can utilize mobile banking applications for online shopping, which may store many sensitive data on their devices. Hence there is a need to authenticate users and detect imposters. However, traditional textual passwords are easily compromised and are not convenient for users to remember for a long time due to long-term memory limitation. To complement textual passwords, behavioral authentication is developed by authenticating a user based on the relevant biometric features. In this work, we focus on simple shape-based behavioral authentication that requires users to draw shape(s) for authentication, and investigate how to design such kind of behavioral authentication in practice. We consider two research questions: (1) whether the authentication accuracy varies with different shapes, and (2) how many shapes can be used to achieve good usability. In the evaluation, we perform two user studies with 60 participants and measure some typical supervised learning classifiers. Based on the results, we provide insights on designing a supervised shape-based behavioral authentication system, as compared with similar schemes. | ||
| 520 | |a Currently, smartphone security has received much more attention as users may use their devices to perform various sensitive tasks. For example, users can utilize mobile banking applications for online shopping, which may store many sensitive data on their devices. Hence there is a need to authenticate users and detect imposters. However, traditional textual passwords are easily compromised and are not convenient for users to remember for a long time due to long-term memory limitation. To complement textual passwords, behavioral authentication is developed by authenticating a user based on the relevant biometric features. In this work, we focus on simple shape-based behavioral authentication that requires users to draw shape(s) for authentication, and investigate how to design such kind of behavioral authentication in practice. We consider two research questions: (1) whether the authentication accuracy varies with different shapes, and (2) how many shapes can be used to achieve good usability. In the evaluation, we perform two user studies with 60 participants and measure some typical supervised learning classifiers. Based on the results, we provide insights on designing a supervised shape-based behavioral authentication system, as compared with similar schemes. | ||
| 650 | 7 | |a User authentication |2 Elsevier | |
| 650 | 7 | |a Behavioral biometric |2 Elsevier | |
| 650 | 7 | |a Shape-based authentication |2 Elsevier | |
| 650 | 7 | |a Supervised learning |2 Elsevier | |
| 650 | 7 | |a Touch dynamics |2 Elsevier | |
| 700 | 1 | |a Wang, Yu |4 oth | |
| 700 | 1 | |a Li, Jin |4 oth | |
| 700 | 1 | |a Xiang, Yang |4 oth | |
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10.1016/j.jisa.2020.102591 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001231.pica (DE-627)ELV052435431 (ELSEVIER)S2214-2126(20)30758-4 DE-627 ger DE-627 rakwb eng 660 VZ 630 640 580 VZ BIODIV DE-30 fid 42.00 bkl Li, Wenjuan verfasserin aut Toward supervised shape-based behavioral authentication on smartphones 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Currently, smartphone security has received much more attention as users may use their devices to perform various sensitive tasks. For example, users can utilize mobile banking applications for online shopping, which may store many sensitive data on their devices. Hence there is a need to authenticate users and detect imposters. However, traditional textual passwords are easily compromised and are not convenient for users to remember for a long time due to long-term memory limitation. To complement textual passwords, behavioral authentication is developed by authenticating a user based on the relevant biometric features. In this work, we focus on simple shape-based behavioral authentication that requires users to draw shape(s) for authentication, and investigate how to design such kind of behavioral authentication in practice. We consider two research questions: (1) whether the authentication accuracy varies with different shapes, and (2) how many shapes can be used to achieve good usability. In the evaluation, we perform two user studies with 60 participants and measure some typical supervised learning classifiers. Based on the results, we provide insights on designing a supervised shape-based behavioral authentication system, as compared with similar schemes. Currently, smartphone security has received much more attention as users may use their devices to perform various sensitive tasks. For example, users can utilize mobile banking applications for online shopping, which may store many sensitive data on their devices. Hence there is a need to authenticate users and detect imposters. However, traditional textual passwords are easily compromised and are not convenient for users to remember for a long time due to long-term memory limitation. To complement textual passwords, behavioral authentication is developed by authenticating a user based on the relevant biometric features. In this work, we focus on simple shape-based behavioral authentication that requires users to draw shape(s) for authentication, and investigate how to design such kind of behavioral authentication in practice. We consider two research questions: (1) whether the authentication accuracy varies with different shapes, and (2) how many shapes can be used to achieve good usability. In the evaluation, we perform two user studies with 60 participants and measure some typical supervised learning classifiers. Based on the results, we provide insights on designing a supervised shape-based behavioral authentication system, as compared with similar schemes. User authentication Elsevier Behavioral biometric Elsevier Shape-based authentication Elsevier Supervised learning Elsevier Touch dynamics Elsevier Wang, Yu oth Li, Jin oth Xiang, Yang oth Enthalten in Elsevier Khadilkar, Aditi ELSEVIER Models of agglomerate growth in fluidized bed reactors: Critical review, status and applications 2014 Amsterdam [u.a.] (DE-627)ELV028549872 volume:55 year:2020 pages:0 https://doi.org/10.1016/j.jisa.2020.102591 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV GBV_ILN_99 GBV_ILN_165 42.00 Biologie: Allgemeines VZ AR 55 2020 0 |
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10.1016/j.jisa.2020.102591 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001231.pica (DE-627)ELV052435431 (ELSEVIER)S2214-2126(20)30758-4 DE-627 ger DE-627 rakwb eng 660 VZ 630 640 580 VZ BIODIV DE-30 fid 42.00 bkl Li, Wenjuan verfasserin aut Toward supervised shape-based behavioral authentication on smartphones 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Currently, smartphone security has received much more attention as users may use their devices to perform various sensitive tasks. For example, users can utilize mobile banking applications for online shopping, which may store many sensitive data on their devices. Hence there is a need to authenticate users and detect imposters. However, traditional textual passwords are easily compromised and are not convenient for users to remember for a long time due to long-term memory limitation. To complement textual passwords, behavioral authentication is developed by authenticating a user based on the relevant biometric features. In this work, we focus on simple shape-based behavioral authentication that requires users to draw shape(s) for authentication, and investigate how to design such kind of behavioral authentication in practice. We consider two research questions: (1) whether the authentication accuracy varies with different shapes, and (2) how many shapes can be used to achieve good usability. In the evaluation, we perform two user studies with 60 participants and measure some typical supervised learning classifiers. Based on the results, we provide insights on designing a supervised shape-based behavioral authentication system, as compared with similar schemes. Currently, smartphone security has received much more attention as users may use their devices to perform various sensitive tasks. For example, users can utilize mobile banking applications for online shopping, which may store many sensitive data on their devices. Hence there is a need to authenticate users and detect imposters. However, traditional textual passwords are easily compromised and are not convenient for users to remember for a long time due to long-term memory limitation. To complement textual passwords, behavioral authentication is developed by authenticating a user based on the relevant biometric features. In this work, we focus on simple shape-based behavioral authentication that requires users to draw shape(s) for authentication, and investigate how to design such kind of behavioral authentication in practice. We consider two research questions: (1) whether the authentication accuracy varies with different shapes, and (2) how many shapes can be used to achieve good usability. In the evaluation, we perform two user studies with 60 participants and measure some typical supervised learning classifiers. Based on the results, we provide insights on designing a supervised shape-based behavioral authentication system, as compared with similar schemes. User authentication Elsevier Behavioral biometric Elsevier Shape-based authentication Elsevier Supervised learning Elsevier Touch dynamics Elsevier Wang, Yu oth Li, Jin oth Xiang, Yang oth Enthalten in Elsevier Khadilkar, Aditi ELSEVIER Models of agglomerate growth in fluidized bed reactors: Critical review, status and applications 2014 Amsterdam [u.a.] (DE-627)ELV028549872 volume:55 year:2020 pages:0 https://doi.org/10.1016/j.jisa.2020.102591 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV GBV_ILN_99 GBV_ILN_165 42.00 Biologie: Allgemeines VZ AR 55 2020 0 |
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10.1016/j.jisa.2020.102591 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001231.pica (DE-627)ELV052435431 (ELSEVIER)S2214-2126(20)30758-4 DE-627 ger DE-627 rakwb eng 660 VZ 630 640 580 VZ BIODIV DE-30 fid 42.00 bkl Li, Wenjuan verfasserin aut Toward supervised shape-based behavioral authentication on smartphones 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Currently, smartphone security has received much more attention as users may use their devices to perform various sensitive tasks. For example, users can utilize mobile banking applications for online shopping, which may store many sensitive data on their devices. Hence there is a need to authenticate users and detect imposters. However, traditional textual passwords are easily compromised and are not convenient for users to remember for a long time due to long-term memory limitation. To complement textual passwords, behavioral authentication is developed by authenticating a user based on the relevant biometric features. In this work, we focus on simple shape-based behavioral authentication that requires users to draw shape(s) for authentication, and investigate how to design such kind of behavioral authentication in practice. We consider two research questions: (1) whether the authentication accuracy varies with different shapes, and (2) how many shapes can be used to achieve good usability. In the evaluation, we perform two user studies with 60 participants and measure some typical supervised learning classifiers. Based on the results, we provide insights on designing a supervised shape-based behavioral authentication system, as compared with similar schemes. Currently, smartphone security has received much more attention as users may use their devices to perform various sensitive tasks. For example, users can utilize mobile banking applications for online shopping, which may store many sensitive data on their devices. Hence there is a need to authenticate users and detect imposters. However, traditional textual passwords are easily compromised and are not convenient for users to remember for a long time due to long-term memory limitation. To complement textual passwords, behavioral authentication is developed by authenticating a user based on the relevant biometric features. In this work, we focus on simple shape-based behavioral authentication that requires users to draw shape(s) for authentication, and investigate how to design such kind of behavioral authentication in practice. We consider two research questions: (1) whether the authentication accuracy varies with different shapes, and (2) how many shapes can be used to achieve good usability. In the evaluation, we perform two user studies with 60 participants and measure some typical supervised learning classifiers. Based on the results, we provide insights on designing a supervised shape-based behavioral authentication system, as compared with similar schemes. User authentication Elsevier Behavioral biometric Elsevier Shape-based authentication Elsevier Supervised learning Elsevier Touch dynamics Elsevier Wang, Yu oth Li, Jin oth Xiang, Yang oth Enthalten in Elsevier Khadilkar, Aditi ELSEVIER Models of agglomerate growth in fluidized bed reactors: Critical review, status and applications 2014 Amsterdam [u.a.] (DE-627)ELV028549872 volume:55 year:2020 pages:0 https://doi.org/10.1016/j.jisa.2020.102591 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV GBV_ILN_99 GBV_ILN_165 42.00 Biologie: Allgemeines VZ AR 55 2020 0 |
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10.1016/j.jisa.2020.102591 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001231.pica (DE-627)ELV052435431 (ELSEVIER)S2214-2126(20)30758-4 DE-627 ger DE-627 rakwb eng 660 VZ 630 640 580 VZ BIODIV DE-30 fid 42.00 bkl Li, Wenjuan verfasserin aut Toward supervised shape-based behavioral authentication on smartphones 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Currently, smartphone security has received much more attention as users may use their devices to perform various sensitive tasks. For example, users can utilize mobile banking applications for online shopping, which may store many sensitive data on their devices. Hence there is a need to authenticate users and detect imposters. However, traditional textual passwords are easily compromised and are not convenient for users to remember for a long time due to long-term memory limitation. To complement textual passwords, behavioral authentication is developed by authenticating a user based on the relevant biometric features. In this work, we focus on simple shape-based behavioral authentication that requires users to draw shape(s) for authentication, and investigate how to design such kind of behavioral authentication in practice. We consider two research questions: (1) whether the authentication accuracy varies with different shapes, and (2) how many shapes can be used to achieve good usability. In the evaluation, we perform two user studies with 60 participants and measure some typical supervised learning classifiers. Based on the results, we provide insights on designing a supervised shape-based behavioral authentication system, as compared with similar schemes. Currently, smartphone security has received much more attention as users may use their devices to perform various sensitive tasks. For example, users can utilize mobile banking applications for online shopping, which may store many sensitive data on their devices. Hence there is a need to authenticate users and detect imposters. However, traditional textual passwords are easily compromised and are not convenient for users to remember for a long time due to long-term memory limitation. To complement textual passwords, behavioral authentication is developed by authenticating a user based on the relevant biometric features. In this work, we focus on simple shape-based behavioral authentication that requires users to draw shape(s) for authentication, and investigate how to design such kind of behavioral authentication in practice. We consider two research questions: (1) whether the authentication accuracy varies with different shapes, and (2) how many shapes can be used to achieve good usability. In the evaluation, we perform two user studies with 60 participants and measure some typical supervised learning classifiers. Based on the results, we provide insights on designing a supervised shape-based behavioral authentication system, as compared with similar schemes. User authentication Elsevier Behavioral biometric Elsevier Shape-based authentication Elsevier Supervised learning Elsevier Touch dynamics Elsevier Wang, Yu oth Li, Jin oth Xiang, Yang oth Enthalten in Elsevier Khadilkar, Aditi ELSEVIER Models of agglomerate growth in fluidized bed reactors: Critical review, status and applications 2014 Amsterdam [u.a.] (DE-627)ELV028549872 volume:55 year:2020 pages:0 https://doi.org/10.1016/j.jisa.2020.102591 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV GBV_ILN_99 GBV_ILN_165 42.00 Biologie: Allgemeines VZ AR 55 2020 0 |
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10.1016/j.jisa.2020.102591 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001231.pica (DE-627)ELV052435431 (ELSEVIER)S2214-2126(20)30758-4 DE-627 ger DE-627 rakwb eng 660 VZ 630 640 580 VZ BIODIV DE-30 fid 42.00 bkl Li, Wenjuan verfasserin aut Toward supervised shape-based behavioral authentication on smartphones 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Currently, smartphone security has received much more attention as users may use their devices to perform various sensitive tasks. For example, users can utilize mobile banking applications for online shopping, which may store many sensitive data on their devices. Hence there is a need to authenticate users and detect imposters. However, traditional textual passwords are easily compromised and are not convenient for users to remember for a long time due to long-term memory limitation. To complement textual passwords, behavioral authentication is developed by authenticating a user based on the relevant biometric features. In this work, we focus on simple shape-based behavioral authentication that requires users to draw shape(s) for authentication, and investigate how to design such kind of behavioral authentication in practice. We consider two research questions: (1) whether the authentication accuracy varies with different shapes, and (2) how many shapes can be used to achieve good usability. In the evaluation, we perform two user studies with 60 participants and measure some typical supervised learning classifiers. Based on the results, we provide insights on designing a supervised shape-based behavioral authentication system, as compared with similar schemes. Currently, smartphone security has received much more attention as users may use their devices to perform various sensitive tasks. For example, users can utilize mobile banking applications for online shopping, which may store many sensitive data on their devices. Hence there is a need to authenticate users and detect imposters. However, traditional textual passwords are easily compromised and are not convenient for users to remember for a long time due to long-term memory limitation. To complement textual passwords, behavioral authentication is developed by authenticating a user based on the relevant biometric features. In this work, we focus on simple shape-based behavioral authentication that requires users to draw shape(s) for authentication, and investigate how to design such kind of behavioral authentication in practice. We consider two research questions: (1) whether the authentication accuracy varies with different shapes, and (2) how many shapes can be used to achieve good usability. In the evaluation, we perform two user studies with 60 participants and measure some typical supervised learning classifiers. Based on the results, we provide insights on designing a supervised shape-based behavioral authentication system, as compared with similar schemes. User authentication Elsevier Behavioral biometric Elsevier Shape-based authentication Elsevier Supervised learning Elsevier Touch dynamics Elsevier Wang, Yu oth Li, Jin oth Xiang, Yang oth Enthalten in Elsevier Khadilkar, Aditi ELSEVIER Models of agglomerate growth in fluidized bed reactors: Critical review, status and applications 2014 Amsterdam [u.a.] (DE-627)ELV028549872 volume:55 year:2020 pages:0 https://doi.org/10.1016/j.jisa.2020.102591 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV GBV_ILN_99 GBV_ILN_165 42.00 Biologie: Allgemeines VZ AR 55 2020 0 |
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Enthalten in Models of agglomerate growth in fluidized bed reactors: Critical review, status and applications Amsterdam [u.a.] volume:55 year:2020 pages:0 |
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Currently, smartphone security has received much more attention as users may use their devices to perform various sensitive tasks. For example, users can utilize mobile banking applications for online shopping, which may store many sensitive data on their devices. Hence there is a need to authenticate users and detect imposters. However, traditional textual passwords are easily compromised and are not convenient for users to remember for a long time due to long-term memory limitation. To complement textual passwords, behavioral authentication is developed by authenticating a user based on the relevant biometric features. In this work, we focus on simple shape-based behavioral authentication that requires users to draw shape(s) for authentication, and investigate how to design such kind of behavioral authentication in practice. We consider two research questions: (1) whether the authentication accuracy varies with different shapes, and (2) how many shapes can be used to achieve good usability. In the evaluation, we perform two user studies with 60 participants and measure some typical supervised learning classifiers. Based on the results, we provide insights on designing a supervised shape-based behavioral authentication system, as compared with similar schemes. |
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Currently, smartphone security has received much more attention as users may use their devices to perform various sensitive tasks. For example, users can utilize mobile banking applications for online shopping, which may store many sensitive data on their devices. Hence there is a need to authenticate users and detect imposters. However, traditional textual passwords are easily compromised and are not convenient for users to remember for a long time due to long-term memory limitation. To complement textual passwords, behavioral authentication is developed by authenticating a user based on the relevant biometric features. In this work, we focus on simple shape-based behavioral authentication that requires users to draw shape(s) for authentication, and investigate how to design such kind of behavioral authentication in practice. We consider two research questions: (1) whether the authentication accuracy varies with different shapes, and (2) how many shapes can be used to achieve good usability. In the evaluation, we perform two user studies with 60 participants and measure some typical supervised learning classifiers. Based on the results, we provide insights on designing a supervised shape-based behavioral authentication system, as compared with similar schemes. |
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Currently, smartphone security has received much more attention as users may use their devices to perform various sensitive tasks. For example, users can utilize mobile banking applications for online shopping, which may store many sensitive data on their devices. Hence there is a need to authenticate users and detect imposters. However, traditional textual passwords are easily compromised and are not convenient for users to remember for a long time due to long-term memory limitation. To complement textual passwords, behavioral authentication is developed by authenticating a user based on the relevant biometric features. In this work, we focus on simple shape-based behavioral authentication that requires users to draw shape(s) for authentication, and investigate how to design such kind of behavioral authentication in practice. We consider two research questions: (1) whether the authentication accuracy varies with different shapes, and (2) how many shapes can be used to achieve good usability. In the evaluation, we perform two user studies with 60 participants and measure some typical supervised learning classifiers. Based on the results, we provide insights on designing a supervised shape-based behavioral authentication system, as compared with similar schemes. |
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