Developing a trust model for pervasive computing based on Apriori association rules learning and Bayesian classification
Abstract Pervasive computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identit...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
D’Angelo, Gianni [verfasserIn] Rampone, Salvatore [verfasserIn] Palmieri, Francesco [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 21(2016), 21 vom: 14. Mai, Seite 6297-6315 |
|---|---|
| Übergeordnetes Werk: |
volume:21 ; year:2016 ; number:21 ; day:14 ; month:05 ; pages:6297-6315 |
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| DOI / URN: |
10.1007/s00500-016-2183-1 |
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| 520 | |a Abstract Pervasive computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more challenging compared to traditional computing environments. In this work, we review these general issues and propose a pervasive computing architecture based on a simple but effective trust model that is better able to cope with them. The proposed architecture combines some artificial intelligence techniques to achieve close resemblance with human-like decision making. Accordingly, Apriori algorithm is first used in order to extract the behavioral patterns adopted from the users during their network interactions. Naïve Bayes classifier is then used for final decision making expressed in term of probability of user trustworthiness. To validate our approach, we applied it to some typical ubiquitous computing scenarios. The obtained results demonstrated the usefulness of such approach and the competitiveness against other existing ones. | ||
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10.1007/s00500-016-2183-1 doi (DE-627)SPR006494455 (SPR)s00500-016-2183-1-e DE-627 ger DE-627 rakwb eng D’Angelo, Gianni verfasserin aut Developing a trust model for pervasive computing based on Apriori association rules learning and Bayesian classification 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Pervasive computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more challenging compared to traditional computing environments. In this work, we review these general issues and propose a pervasive computing architecture based on a simple but effective trust model that is better able to cope with them. The proposed architecture combines some artificial intelligence techniques to achieve close resemblance with human-like decision making. Accordingly, Apriori algorithm is first used in order to extract the behavioral patterns adopted from the users during their network interactions. Naïve Bayes classifier is then used for final decision making expressed in term of probability of user trustworthiness. To validate our approach, we applied it to some typical ubiquitous computing scenarios. The obtained results demonstrated the usefulness of such approach and the competitiveness against other existing ones. Pervasive computing (dpeaa)DE-He213 Trust model (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Apriori algorithm (dpeaa)DE-He213 Naïve Bayes classifier (dpeaa)DE-He213 Rampone, Salvatore verfasserin aut Palmieri, Francesco verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 21(2016), 21 vom: 14. Mai, Seite 6297-6315 (DE-627)SPR006469531 nnns volume:21 year:2016 number:21 day:14 month:05 pages:6297-6315 https://dx.doi.org/10.1007/s00500-016-2183-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 21 2016 21 14 05 6297-6315 |
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10.1007/s00500-016-2183-1 doi (DE-627)SPR006494455 (SPR)s00500-016-2183-1-e DE-627 ger DE-627 rakwb eng D’Angelo, Gianni verfasserin aut Developing a trust model for pervasive computing based on Apriori association rules learning and Bayesian classification 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Pervasive computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more challenging compared to traditional computing environments. In this work, we review these general issues and propose a pervasive computing architecture based on a simple but effective trust model that is better able to cope with them. The proposed architecture combines some artificial intelligence techniques to achieve close resemblance with human-like decision making. Accordingly, Apriori algorithm is first used in order to extract the behavioral patterns adopted from the users during their network interactions. Naïve Bayes classifier is then used for final decision making expressed in term of probability of user trustworthiness. To validate our approach, we applied it to some typical ubiquitous computing scenarios. The obtained results demonstrated the usefulness of such approach and the competitiveness against other existing ones. Pervasive computing (dpeaa)DE-He213 Trust model (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Apriori algorithm (dpeaa)DE-He213 Naïve Bayes classifier (dpeaa)DE-He213 Rampone, Salvatore verfasserin aut Palmieri, Francesco verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 21(2016), 21 vom: 14. Mai, Seite 6297-6315 (DE-627)SPR006469531 nnns volume:21 year:2016 number:21 day:14 month:05 pages:6297-6315 https://dx.doi.org/10.1007/s00500-016-2183-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 21 2016 21 14 05 6297-6315 |
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10.1007/s00500-016-2183-1 doi (DE-627)SPR006494455 (SPR)s00500-016-2183-1-e DE-627 ger DE-627 rakwb eng D’Angelo, Gianni verfasserin aut Developing a trust model for pervasive computing based on Apriori association rules learning and Bayesian classification 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Pervasive computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more challenging compared to traditional computing environments. In this work, we review these general issues and propose a pervasive computing architecture based on a simple but effective trust model that is better able to cope with them. The proposed architecture combines some artificial intelligence techniques to achieve close resemblance with human-like decision making. Accordingly, Apriori algorithm is first used in order to extract the behavioral patterns adopted from the users during their network interactions. Naïve Bayes classifier is then used for final decision making expressed in term of probability of user trustworthiness. To validate our approach, we applied it to some typical ubiquitous computing scenarios. The obtained results demonstrated the usefulness of such approach and the competitiveness against other existing ones. Pervasive computing (dpeaa)DE-He213 Trust model (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Apriori algorithm (dpeaa)DE-He213 Naïve Bayes classifier (dpeaa)DE-He213 Rampone, Salvatore verfasserin aut Palmieri, Francesco verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 21(2016), 21 vom: 14. Mai, Seite 6297-6315 (DE-627)SPR006469531 nnns volume:21 year:2016 number:21 day:14 month:05 pages:6297-6315 https://dx.doi.org/10.1007/s00500-016-2183-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 21 2016 21 14 05 6297-6315 |
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10.1007/s00500-016-2183-1 doi (DE-627)SPR006494455 (SPR)s00500-016-2183-1-e DE-627 ger DE-627 rakwb eng D’Angelo, Gianni verfasserin aut Developing a trust model for pervasive computing based on Apriori association rules learning and Bayesian classification 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Pervasive computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more challenging compared to traditional computing environments. In this work, we review these general issues and propose a pervasive computing architecture based on a simple but effective trust model that is better able to cope with them. The proposed architecture combines some artificial intelligence techniques to achieve close resemblance with human-like decision making. Accordingly, Apriori algorithm is first used in order to extract the behavioral patterns adopted from the users during their network interactions. Naïve Bayes classifier is then used for final decision making expressed in term of probability of user trustworthiness. To validate our approach, we applied it to some typical ubiquitous computing scenarios. The obtained results demonstrated the usefulness of such approach and the competitiveness against other existing ones. Pervasive computing (dpeaa)DE-He213 Trust model (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Apriori algorithm (dpeaa)DE-He213 Naïve Bayes classifier (dpeaa)DE-He213 Rampone, Salvatore verfasserin aut Palmieri, Francesco verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 21(2016), 21 vom: 14. Mai, Seite 6297-6315 (DE-627)SPR006469531 nnns volume:21 year:2016 number:21 day:14 month:05 pages:6297-6315 https://dx.doi.org/10.1007/s00500-016-2183-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 21 2016 21 14 05 6297-6315 |
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Abstract Pervasive computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more challenging compared to traditional computing environments. In this work, we review these general issues and propose a pervasive computing architecture based on a simple but effective trust model that is better able to cope with them. The proposed architecture combines some artificial intelligence techniques to achieve close resemblance with human-like decision making. Accordingly, Apriori algorithm is first used in order to extract the behavioral patterns adopted from the users during their network interactions. Naïve Bayes classifier is then used for final decision making expressed in term of probability of user trustworthiness. To validate our approach, we applied it to some typical ubiquitous computing scenarios. The obtained results demonstrated the usefulness of such approach and the competitiveness against other existing ones. |
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Abstract Pervasive computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more challenging compared to traditional computing environments. In this work, we review these general issues and propose a pervasive computing architecture based on a simple but effective trust model that is better able to cope with them. The proposed architecture combines some artificial intelligence techniques to achieve close resemblance with human-like decision making. Accordingly, Apriori algorithm is first used in order to extract the behavioral patterns adopted from the users during their network interactions. Naïve Bayes classifier is then used for final decision making expressed in term of probability of user trustworthiness. To validate our approach, we applied it to some typical ubiquitous computing scenarios. The obtained results demonstrated the usefulness of such approach and the competitiveness against other existing ones. |
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Abstract Pervasive computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more challenging compared to traditional computing environments. In this work, we review these general issues and propose a pervasive computing architecture based on a simple but effective trust model that is better able to cope with them. The proposed architecture combines some artificial intelligence techniques to achieve close resemblance with human-like decision making. Accordingly, Apriori algorithm is first used in order to extract the behavioral patterns adopted from the users during their network interactions. Naïve Bayes classifier is then used for final decision making expressed in term of probability of user trustworthiness. To validate our approach, we applied it to some typical ubiquitous computing scenarios. The obtained results demonstrated the usefulness of such approach and the competitiveness against other existing ones. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR006494455</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124002831.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-016-2183-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR006494455</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-016-2183-1-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">D’Angelo, Gianni</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Developing a trust model for pervasive computing based on Apriori association rules learning and Bayesian classification</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Pervasive computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more challenging compared to traditional computing environments. In this work, we review these general issues and propose a pervasive computing architecture based on a simple but effective trust model that is better able to cope with them. The proposed architecture combines some artificial intelligence techniques to achieve close resemblance with human-like decision making. Accordingly, Apriori algorithm is first used in order to extract the behavioral patterns adopted from the users during their network interactions. Naïve Bayes classifier is then used for final decision making expressed in term of probability of user trustworthiness. To validate our approach, we applied it to some typical ubiquitous computing scenarios. 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Mai, Seite 6297-6315</subfield><subfield code="w">(DE-627)SPR006469531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:21</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:21</subfield><subfield code="g">day:14</subfield><subfield code="g">month:05</subfield><subfield code="g">pages:6297-6315</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-016-2183-1</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">21</subfield><subfield code="j">2016</subfield><subfield code="e">21</subfield><subfield code="b">14</subfield><subfield code="c">05</subfield><subfield code="h">6297-6315</subfield></datafield></record></collection>
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