Presented at
DeepSec 2017 „Science First!“,
Unknown date/time
(Unknown duration)
Detection of binary functions in compiled code is a major stepping
stone towards any advanced binary analysis technique. Nucleus [1]
is a novel algorithm based on the idea of using the interprocedural
control flow graph to detect function boundaries. Building upon this
technology we propose a new approach to solve the related problem
of identifying previously-seen known functions within a binary.
Our idea is based on comparing the control flow graphs (CFGs)
of unknown functions from a binary to known functions from a
previously generated database. Compared to traditional approaches,
our method is aware of the underlying graph matching problem
being performed on CFGs of binary code: First, it utilizes instruction
level knowledge about basic blocks as additional constraints for
graph isomorphism. Second, optimizations and transformations
introduced by different compilers affecting the shape of the CFG
are taken into account.
Our approach aims to avoid false positives (wrongly assigning a
known function symbol to an unknown function) at all cost: The
evaluation shows that this method is very effective in reducing false
positive matches (below one percent in most cases) maintaining
recall rates as high as 72.8% when matching functions across two
different nginx versions (1.12.1 and 1.10.3).
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