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Stakhanova, Natalia, Basu, Samik, Lutz, Robyn and Wong, Johnny (2006) Automated caching of behavioral patterns for efficient run-time. Technical Report 06-06, Computer Science, Iowa State University.
Abstract
Run-time monitoring is a powerful approach for dy-
namically detecting faults or malicious activity of
software systems. However, there are often two obsta-
cles to the implementation of this approach in prac-
tice: (1) that developing correct and/or faulty be-
havioral patterns can be a difficult, labor-intensive
process, and (2) that use of such pattern-monitoring
must provide rapid turn-around or response time. We
present a novel data structure, called extended action
graph, and associated algorithms to overcome these
drawbacks. At its core, our technique relies on ef-
fectively identifying and caching specifications from
(correct/faulty) patterns learnt via machine-learning
algorithm. We describe the design and implementa-
tion of our technique and show its practical applicabil-
ity in the domain of security monitoring of sendmail
software.
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