Environment-sensitive intrusion detection

  • Authors:
  • Jonathon T. Giffin;David Dagon;Somesh Jha;Wenke Lee;Barton P. Miller

  • Affiliations:
  • Computer Sciences Department, University of Wisconsin;College of Computing, Georgia Institute of Technology;Computer Sciences Department, University of Wisconsin;College of Computing, Georgia Institute of Technology;Computer Sciences Department, University of Wisconsin

  • Venue:
  • RAID'05 Proceedings of the 8th international conference on Recent Advances in Intrusion Detection
  • Year:
  • 2005

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Abstract

We perform host-based intrusion detection by constructing a model from a program's binary code and then restricting the program's execution by the model. We improve the effectiveness of such model-based intrusion detection systems by incorporating into the model knowledge of the environment in which the program runs, and by increasing the accuracy of our models with a new data-flow analysis algorithm for context-sensitive recovery of static data. The environment—configuration files, command-line parameters, and environment variables—constrains acceptable process execution. Environment dependencies added to a program model update the model to the current environment at every program execution. Our new static data-flow analysis associates a program's data flows with specific calling contexts that use the data. We use this analysis to differentiate system-call arguments flowing from distinct call sites in the program. Using a new average reachability measure suitable for evaluation of call-stack-based program models, we demonstrate that our techniques improve the precision of several test programs' models from 76% to 100%.