Learning to Share Distributed Probabilistic Beliefs

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Learning to Share Distributed Probabilistic Beliefs
In this paper, we present a general machine learning approach to the problem of deciding when to share probabilistic beliefs between agents for distributed monitoring. Our approach can generally be applied to domains that use a probabilistic model for evaluating hypotheses, and have a method for combining beliefs from multiple agents. We demonstrate the effectiveness of our approach in a concrete application in network intrusion detection as an example of a multi-agent monitoring problem. Based on an evaluation using packet trace data from a real network, we demonstrate that our learning approach can reduce both the delay and communication overhead required to detect network intrusions.
Christopher Leckie, Kotagiri Ramamohanarao
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2002
Where ICML
Authors Christopher Leckie, Kotagiri Ramamohanarao
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