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Unpublished Paper
Automated caching of behavioral patterns for efficient run-time
Computer Science Technical Reports
  • Natalia Stakhanova, Iowa State University
  • Samik Basu, Iowa State University
  • Robyn R. Lutz, Iowa State University
  • Johnny S. Wong, Iowa State University
Publication Date
Technical Report Number
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.
Citation Information
Natalia Stakhanova, Samik Basu, Robyn R. Lutz and Johnny S. Wong. "Automated caching of behavioral patterns for efficient run-time" (2006)
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