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
Gespeichert in:
| Autor*in: |
Li, Wenjuan [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Umfang: |
7 |
|---|
| 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.] |
|---|---|
| Übergeordnetes Werk: |
volume:144 ; year:2021 ; pages:35-41 ; extent:7 |
| Links: |
|---|
| 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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• 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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code="c">2021</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">7</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</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 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