  We address the problem of reverse engineering of stripped executables, which
contain no debug information. This is a challenging problem because of the low
amount of syntactic information available in stripped executables, and the
diverse assembly code patterns arising from compiler optimizations.
  We present a novel approach for predicting procedure names in stripped
executables. Our approach combines static analysis with neural models. The main
idea is to use static analysis to obtain augmented representations of call
sites; encode the structure of these call sites using the control-flow graph
(CFG) and finally, generate a target name while attending to these call sites.
We use our representation to drive graph-based, LSTM-based and
Transformer-based architectures.
  Our evaluation shows that our models produce predictions that are difficult
and time consuming for humans, while improving on existing methods by 28% and
by 100% over state-of-the-art neural textual models that do not use any static
analysis. Code and data for this evaluation are available at
https://github.com/tech-srl/Nero .
