This research encompasses a preliminary model for a novel learning based approach to network awareness.

Bayesian Network Awareness BoF

Super Computing Reno 2007 - BNA BoF PPT

A short presentation and open discussion of distributed learning approaches to network awareness for supercomputing.

Time permitting we will consider the: potential difficulties of applying emerging artificial intelligence techniques to learn normal behavior and identify problematic activities; possible strategies for feeding sensor nodes with summarized parameters; issues around timely application of dynamic views.

The use of learning to, in an ongoing unsupervised manner, characterize the detailed normal behavior of packets, conversations and data streams which may be represented as vectors of scalars, opens up the possibility of rapid identification of subtle abnormal activity. Clearly derived views depend heavily on monitor stack ability to supply sensor nodes with appropriate and timely data.

Practical scalability and performance requirements imply a high degree of autonomous adaptation in a complex distributed environment.