Exploring touch-based behavioral authentication on smartphone email applications in IoT-enabled smart cities
• We study users’ touch behavior when using Email applications on smartphones. • In our user study, we collect the data from a total of 60 participants. • We consider three scenarios: free task, Email usage task, and SocialAuth. • We test five supervised learning classifiers such as J48, RBFN, BPNN,...
Ausführliche Beschreibung
Autor*in: |
Li, Wenjuan [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2021 |
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Umfang: |
7 |
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Übergeordnetes Werk: |
Enthalten in: Thermal structure optimization of a supercondcuting cavity vertical test cryostat - Jin, Shufeng ELSEVIER, 2019, an official publ. of the International Association for Pattern Recognition, Amsterdam [u.a.] |
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Übergeordnetes Werk: |
volume:144 ; year:2021 ; pages:35-41 ; extent:7 |
Links: |
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DOI / URN: |
10.1016/j.patrec.2021.01.019 |
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520 | |a • We study users’ touch behavior when using Email applications on smartphones. • In our user study, we collect the data from a total of 60 participants. • We consider three scenarios: free task, Email usage task, and SocialAuth. • We test five supervised learning classifiers such as J48, RBFN, BPNN, SVM and NB. • SVM can outperform others with an average error rate of around 2.9% under Email usage. | ||
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10.1016/j.patrec.2021.01.019 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001746.pica (DE-627)ELV053236955 (ELSEVIER)S0167-8655(21)00032-5 DE-627 ger DE-627 rakwb eng 660 VZ 52.43 bkl 33.09 bkl Li, Wenjuan verfasserin aut Exploring touch-based behavioral authentication on smartphone email applications in IoT-enabled smart cities 2021 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • We study users’ touch behavior when using Email applications on smartphones. • In our user study, we collect the data from a total of 60 participants. • We consider three scenarios: free task, Email usage task, and SocialAuth. • We test five supervised learning classifiers such as J48, RBFN, BPNN, SVM and NB. • SVM can outperform others with an average error rate of around 2.9% under Email usage. Meng, Weizhi oth Furnell, Steven oth Enthalten in Elsevier Jin, Shufeng ELSEVIER Thermal structure optimization of a supercondcuting cavity vertical test cryostat 2019 an official publ. of the International Association for Pattern Recognition Amsterdam [u.a.] (DE-627)ELV003173968 volume:144 year:2021 pages:35-41 extent:7 https://doi.org/10.1016/j.patrec.2021.01.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 52.43 Kältetechnik VZ 33.09 Physik unter besonderen Bedingungen VZ AR 144 2021 35-41 7 |
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10.1016/j.patrec.2021.01.019 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001746.pica (DE-627)ELV053236955 (ELSEVIER)S0167-8655(21)00032-5 DE-627 ger DE-627 rakwb eng 660 VZ 52.43 bkl 33.09 bkl Li, Wenjuan verfasserin aut Exploring touch-based behavioral authentication on smartphone email applications in IoT-enabled smart cities 2021 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • We study users’ touch behavior when using Email applications on smartphones. • In our user study, we collect the data from a total of 60 participants. • We consider three scenarios: free task, Email usage task, and SocialAuth. • We test five supervised learning classifiers such as J48, RBFN, BPNN, SVM and NB. • SVM can outperform others with an average error rate of around 2.9% under Email usage. Meng, Weizhi oth Furnell, Steven oth Enthalten in Elsevier Jin, Shufeng ELSEVIER Thermal structure optimization of a supercondcuting cavity vertical test cryostat 2019 an official publ. of the International Association for Pattern Recognition Amsterdam [u.a.] (DE-627)ELV003173968 volume:144 year:2021 pages:35-41 extent:7 https://doi.org/10.1016/j.patrec.2021.01.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 52.43 Kältetechnik VZ 33.09 Physik unter besonderen Bedingungen VZ AR 144 2021 35-41 7 |
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• We study users’ touch behavior when using Email applications on smartphones. • In our user study, we collect the data from a total of 60 participants. • We consider three scenarios: free task, Email usage task, and SocialAuth. • We test five supervised learning classifiers such as J48, RBFN, BPNN, SVM and NB. • SVM can outperform others with an average error rate of around 2.9% under Email usage. |
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• We study users’ touch behavior when using Email applications on smartphones. • In our user study, we collect the data from a total of 60 participants. • We consider three scenarios: free task, Email usage task, and SocialAuth. • We test five supervised learning classifiers such as J48, RBFN, BPNN, SVM and NB. • SVM can outperform others with an average error rate of around 2.9% under Email usage. |
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• We study users’ touch behavior when using Email applications on smartphones. • In our user study, we collect the data from a total of 60 participants. • We consider three scenarios: free task, Email usage task, and SocialAuth. • We test five supervised learning classifiers such as J48, RBFN, BPNN, SVM and NB. • SVM can outperform others with an average error rate of around 2.9% under Email usage. |
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• SVM can outperform others with an average error rate of around 2.9% under Email usage.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Meng, Weizhi</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Furnell, Steven</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Jin, Shufeng ELSEVIER</subfield><subfield code="t">Thermal structure optimization of a supercondcuting cavity vertical test cryostat</subfield><subfield code="d">2019</subfield><subfield code="d">an official publ. of the International Association for Pattern Recognition</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV003173968</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:144</subfield><subfield code="g">year:2021</subfield><subfield code="g">pages:35-41</subfield><subfield code="g">extent:7</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.patrec.2021.01.019</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">52.43</subfield><subfield code="j">Kältetechnik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">33.09</subfield><subfield code="j">Physik unter besonderen Bedingungen</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">144</subfield><subfield code="j">2021</subfield><subfield code="h">35-41</subfield><subfield code="g">7</subfield></datafield></record></collection>
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