Hauke, Sascha ; Biedermann, Sebastian ; Mühlhäuser, Max ; Heider, Dominik (2013):
On the Application of the Supervised Machine Learning to Trustworthiness Assessment.
In: Proceedings: 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications: TrustCom 2013, pp. 525 - 534,
IEEE, 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, Melbourne, Australia, 16.-18.07.2013, ISBN 978-0-7695-5022-0,
DOI: 10.1109/TrustCom.2013.5,
[Conference or Workshop Item]
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.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2013 |
| Creators: | Hauke, Sascha ; Biedermann, Sebastian ; Mühlhäuser, Max ; Heider, Dominik |
| Title: | On the Application of the Supervised Machine Learning to Trustworthiness Assessment |
| Language: | English |
| 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. |
| Journal or Publication Title: | Proceedings of the 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-13) |
| Book Title: | Proceedings: 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications: TrustCom 2013 |
| Publisher: | IEEE |
| ISBN: | 978-0-7695-5022-0 |
| Uncontrolled Keywords: | - SST: CASED:, - SST - Area Smart Security and Trust |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Telecooperation LOEWE LOEWE > LOEWE-Zentren LOEWE > LOEWE-Zentren > CASED – Center for Advanced Security Research Darmstadt |
| Event Title: | 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications |
| Event Location: | Melbourne, Australia |
| Event Dates: | 16.-18.07.2013 |
| Date Deposited: | 20 Apr 2015 15:41 |
| DOI: | 10.1109/TrustCom.2013.5 |
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