
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
Type
Communication / ConférenceDate
2021Conference title
36th IEEE/ACM International Conference on Automated Software Engineering (ASE 2021)Conference date
2021-11Conference city
MelbourneConference country
AustraliaBook 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
Metadata
Show full item recordAuthor(s)
Mengin, ElieStatistique, 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 PropagationRelated items
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