Risk assessment Risk probability Cluster Neural network ART BP
Trinity College Dublin, Department of Computer Science
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
Computer Science Technical Report TCD-CS-2003-17
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.
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