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Private computing on a public platform (PCPP) is a new paradigm in public computing in which an application executes on a previously unknown remote system securely and privately. The first step in the PCPP process is remote assessment of a prospective remote host to determine whether it is capable of executing the PCPP application and to classify the host as a potential threat or non-threat. This paper explores the use of a naive Bayesian classifier to classify prospective remote hosts. We show that the naive Bayesian classifier learns to recognize subtle patterns in historical host measurements and performs the classification task accurately and with minimal negative performance implications.
Date of Conference: 26-30 March 2007