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arXiv · 1702.07103

Discriminating Traces with Time

Saeid Tizpaz-Niari·Pavol Cerny·Bor-Yuh Evan Chang·Sriram Sankaranarayanan·Ashutosh Trivedi
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Abstract

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.

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BibTeXRIS

Saeid Tizpaz-Niari, Pavol Cerny, Bor-Yuh Evan Chang, Sriram Sankaranarayanan, Ashutosh Trivedi. 2017-02-23. Discriminating Traces with Time. https://arxiv.org/abs/1702.07103

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