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Quantifying Application Behavior Space for Detection and Self-Healing

Locasto, Michael E.; Stavrou, Angelos; Cretu, Gabriela F.; Keromytis, Angelos D.; Stolfo, Salvatore

The increasing sophistication of software attacks has created the need for increasingly finer-grained intrusion and anomaly detection systems, both at the network and the host level. We believe that the next generation of defense mechanisms will require a much more detailed dynamic analysis of application behavior than is currently done. We also note that the same type of behavior analysis is needed by the current embryonic attempts at self-healing systems. Because such mechanisms are currently perceived as too expensive in terms of their performance impact, questions relating to the feasibility and value of such analysis remain unexplored and unanswered. We present a new mechanism for profiling the behavior space of an application by analyzing all function calls made by the process, including regular functions and library calls, as well as system calls. We derive behavior from aspects of both control and data flow. We show how to build and check profiles that contain this information at the binary level -- that is, without making changes to the application's source, the operating system, or the compiler. This capability makes our system, Lugrind, applicable to a variety of software, including COTS applications. Profiles built for the applications we tested can predict behavior with 97% accuracy given a context window of 15 functions. Lugrind demonstrates the feasibility of combining binary-level behavior profiling with detection and automated repair.

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Academic Units
Computer Science
Publisher
Department of Computer Science, Columbia University
Series
Columbia University Computer Science Technical Reports, CUCS-017-06
Published Here
April 27, 2011
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