Hauke, Sascha ; Biedermann, Sebastian ; Mühlhäuser, Max ; Heider, Dominik (2013)
On the Application of the Supervised Machine Learning to Trustworthiness Assessment.
12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications. Melbourne, Australia (16.-18.07.2013)
doi: 10.1109/TrustCom.2013.5
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
State-of-the art trust and reputation systems seek to apply machine learning methods to overcome generalizability issues of experience-based Bayesian trust assessment. These approaches are, however, often model-centric instead of focussing on data and the complex adaptive system that is driven by reputation-based service selection. This entails the risk of unrealistic model assumptions. We outline the requirements for robust probabilistic trust assessment using supervised learning and apply a selection of estimators to a real-world data set, in order to show the effectiveness of supervised methods. Furthermore, we provide a representational mapping of estimator output to a belief logic representation for the modular integration of supervised methods with other trust assessment methodologies.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2013 |
| Autor(en): | Hauke, Sascha ; Biedermann, Sebastian ; Mühlhäuser, Max ; Heider, Dominik |
| Art des Eintrags: | Bibliographie |
| Titel: | On the Application of the Supervised Machine Learning to Trustworthiness Assessment |
| Sprache: | Englisch |
| Publikationsjahr: | Juli 2013 |
| Verlag: | IEEE |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Proceedings of the 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-13) |
| Buchtitel: | Proceedings: 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications: TrustCom 2013 |
| Veranstaltungstitel: | 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications |
| Veranstaltungsort: | Melbourne, Australia |
| Veranstaltungsdatum: | 16.-18.07.2013 |
| DOI: | 10.1109/TrustCom.2013.5 |
| Kurzbeschreibung (Abstract): | State-of-the art trust and reputation systems seek to apply machine learning methods to overcome generalizability issues of experience-based Bayesian trust assessment. These approaches are, however, often model-centric instead of focussing on data and the complex adaptive system that is driven by reputation-based service selection. This entails the risk of unrealistic model assumptions. We outline the requirements for robust probabilistic trust assessment using supervised learning and apply a selection of estimators to a real-world data set, in order to show the effectiveness of supervised methods. Furthermore, we provide a representational mapping of estimator output to a belief logic representation for the modular integration of supervised methods with other trust assessment methodologies. |
| Freie Schlagworte: | - SST: CASED:, - SST - Area Smart Security and Trust |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Telekooperation LOEWE LOEWE > LOEWE-Zentren LOEWE > LOEWE-Zentren > CASED – Center for Advanced Security Research Darmstadt |
| Hinterlegungsdatum: | 20 Apr 2015 15:41 |
| Letzte Änderung: | 20 Jul 2021 09:50 |
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