Discriminating Traces with Time

  • Saeid Tizpaz-NiariEmail author
  • Pavol Černý
  • Bor-Yuh Evan Chang
  • Sriram Sankaranarayanan
  • Ashutosh Trivedi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10206)


What properties about the internals of a program explain the possible differences in its overall running time for different inputs? In this paper, we propose a formal framework for considering this question we dub trace-set discrimination. We show that even though the algorithmic problem of computing maximum likelihood discriminants is NP-hard, approaches based on integer linear programming (ILP) and decision tree learning can be useful in zeroing-in on the program internals. On a set of Java benchmarks, we find that compactly-represented decision trees scalably discriminate with high accuracy—more scalably than maximum likelihood discriminants and with comparable accuracy. We demonstrate on three larger case studies how decision-tree discriminants produced by our tool are useful for debugging timing side-channel vulnerabilities (i.e., where a malicious observer infers secrets simply from passively watching execution times) and availability vulnerabilities.


Decision Tree Execution Time Integer Linear Programming Execution Trace Decision Tree Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Saeid Tizpaz-Niari
    • 1
    Email author
  • Pavol Černý
    • 1
  • Bor-Yuh Evan Chang
    • 1
  • Sriram Sankaranarayanan
    • 1
  • Ashutosh Trivedi
    • 1
  1. 1.University of Colorado BoulderBoulderUSA

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