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Title: Risk Probability Estimating Based on Clustering
Author: Chen, Yong
Jensen, Christian D.
Gray, Elizabeth
Seigneur, Jean-Marc
Keywords: Risk assessment
Risk probability
Neural network
Issue Date: 21-May-2003
Publisher: Trinity College Dublin, Department of Computer Science
Citation: Chen, Yong; Jensen, Christian Damsgaard; Gray, Elizabeth; Seigneur, Jean-Marc. 'Risk Probability Estimating Based on Clustering'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2003-17, 2003, pp5
Series/Report no.: Computer Science Technical Report
Abstract: Ubiquitous computing environments are highly dynamic, with new unforeseen circumstances and constantly changing environments, which introduces new risks that cannot be assessed through traditional means of risk analysis. Mobile entities in a ubiquitous computing environment require the ability to perform an autonomous assessment of the risk incurred by a specific interaction with another entity in a given context. This assessment will allow a mobile entity to decide whether sufficient evidence exists to mitigate the risk and allow the interaction to proceed. Such evidence might include records of prior experiences, recommendations from a trusted entity or the reputation of the other entity. In this paper we propose a dynamic mechanism for estimating the risk probability of a certain interaction in a given environment using hybrid neural networks. We argue that traditional risk assessment models from the insurance industry do not directly apply to ubiquitous computing environments. Instead, we propose a dynamic mechanism for risk assessment, which is based on pattern matching, classification and prediction procedures. This mechanism uses an estimator of risk probability, which is based on the automatic clustering of defining features of the environment and the other entity, which helps avoid subjective judgments as much as possible.
Appears in Collections:Computer Science Technical Reports

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