DocumentCode :
2792765
Title :
PCPP: On Remote Host Assessment via Naive Bayesian Classification
Author :
Morris, Thomas H. ; Nair, V.S.S.
Author_Institution :
High Assurance Comput. & Networking Lab, Southern Methodist Univ., Dallas, TX
fYear :
2007
fDate :
26-30 March 2007
Firstpage :
1
Lastpage :
8
Abstract :
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.
Keywords :
client-server systems; computer networks; pattern classification; telecommunication security; naive Bayesian classifier; private computing-on-public platform; public computing; remote host assessment; Access control; Bayesian methods; Computer networks; Cryptography; Data security; Grid computing; Local government; Mobile agents; Pattern recognition; Performance evaluation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Parallel and Distributed Processing Symposium, 2007. IPDPS 2007. IEEE International
Conference_Location :
Long Beach, CA
Print_ISBN :
1-4244-0910-1
Electronic_ISBN :
1-4244-0910-1
Type :
conf
DOI :
10.1109/IPDPS.2007.370619
Filename :
4228347
Link To Document :
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