Behavioral distance for intrusion detection

  • Authors:
  • Debin Gao;Michael K. Reiter;Dawn Song

  • Affiliations:
  • Electrical & Computer Engineering Department, Carnegie Mellon University, Pittsburgh, Pennsylvania;Electrical & Computer Engineering Department, Computer Science Department, and CyLab, Carnegie Mellon University, Pittsburgh, Pennsylvania;Electrical & Computer Engineering Department, Computer Science Department, and CyLab, Carnegie Mellon University, Pittsburgh, Pennsylvania

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

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Abstract

We introduce a notion, behavioral distance, for evaluating the extent to which processes—potentially running different programs and executing on different platforms—behave similarly in response to a common input. We explore behavioral distance as a means to detect an attack on one process that causes its behavior to deviate from that of another. We propose a measure of behavioral distance and a realization of this measure using the system calls emitted by processes. Through an empirical evaluation of this measure using three web servers on two different platforms (Linux and Windows), we demonstrate that this approach holds promise for better intrusion detection with moderate overhead.