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Binary Diffing as a Network Alignment Problem via Belief Propagation

Mengin, Elie; Rossi, Fabrice (2021), Binary Diffing as a Network Alignment Problem via Belief Propagation, 36th IEEE/ACM International Conference on Automated Software Engineering (ASE), IEEE - Institute of Electrical and Electronics Engineers : Piscataway, NJ, p. 967 - 978. 10.1109/ASE51524.2021.9678782

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menginrossi2021binary-diffing.pdf (829.6Kb)
Type
Communication / Conférence
Date
2021
Conference title
36th IEEE/ACM International Conference on Automated Software Engineering (ASE 2021)
Conference date
2021-11
Conference city
Melbourne
Conference country
Australia
Book title
36th IEEE/ACM International Conference on Automated Software Engineering (ASE)
Publisher
IEEE - Institute of Electrical and Electronics Engineers
Published in
Piscataway, NJ
Pages
967 - 978
Publication identifier
10.1109/ASE51524.2021.9678782
Metadata
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Author(s)
Mengin, Elie
Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) [SAMM]
Rossi, Fabrice
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Abstract (EN)
In this paper, we address the problem of finding a correspondence, or matching, between the functions of two programs in binary form, which is one of the most common task in binary diffing. We introduce a new formulation of this problem as a particular instance of a graph edit problem over the call graphs of the programs. In this formulation, the quality of a mapping is evaluated simultaneously with respect to both function content and call graph similarities. We show that this formulation is equivalent to a network alignment problem. We propose a solving strategy for this problem based on max-product belief propagation. Finally, we implement a prototype of our method, called QBinDiff, and propose an extensive evaluation which shows that our approach outperforms state of the art diffing tools.
Subjects / Keywords
Binary Diffing; Binary Program Analysis; Graph Edit Distance; Network Alignment; Belief Propagation

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