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Software Obfuscation Technique based on Recurrent Neural Network

Adwan Yasin, Ihab Nassra

Abstract


Protecting intellectual property against tampering is
an urgent issue to many software designers, where illegal access
to sensitive data is considered as a form of copyright
infringement. Software owners apply various protection
techniques in order to address this issue. Many of used
mechanisms are weak, since they are vulnerable to both
dynamic and static analysis, where the other are very costly
since they impose considerable performance penalties.
In this paper, we proposed a data and control flow obfuscating
technique based on integrating encryption mechanism within
recurrent neural network (RNN). Neural network provides a
robust security characteristic in software protection, due to its
ability of representing nonlinear algorithms with a powerful
computational capability. The system is designed to enable the
neural network generating of different encryptions for the same
protected data. This creates a many to one relationship between
the keys and the encryption. Thus, we train the neural network
to simulate the conditional behaviors of the program in order to
complicate the reverse engineering analysis of the software and
hindering the “Concolic testing” attack. Consequently, we
replace the critical points of program’s data and control flows
with a semantically equivalent neural network. Our method is
designed to enable the neural network to execute the conditional
control transfers, where the complexity of neural network
ensures that the protected behavior is turned into a complicated
and incomprehensible form, making it impossible to extract its
rules or locating the accurate inputs that lead to the execution
paths behind the network. The protection presented by our
mechanism is robust against both static and dynamic analysis.
Furthermore, our evaluations confirm that employing the neural
networks in our system significantly increase the difficulties in
revealing the obfuscated software.


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