RT journal article
T1 Source code obfuscation with genetic algorithms using LLVM code optimizations
A1 Torre Macías, Juan Carlos de la
A1 Jareño Dorado, Javier
A1 Aragón Jurado, José Miguel
A1 Varrette, Sébastien
A1 Dorronsoro Díaz, Bernabé
A2 Ingeniería Informática
K1 Source code obfuscation
K1 LLVM
K1 Intermediate representation
K1 Genetic algorithms
K1 Multi-objective optimization
AB With the advent of the cloud computing model allowing a shared access to massive computing facilities, a surging demand emerges for the protection of the intellectual property tied to the programs executed on these uncontrolled systems. If novel paradigm as confidential computing aims at protecting the data manipulated during the execution, obfuscating techniques (in particular at the source code level) remain a popular solution to conceal the purpose of a program or its logic without altering its functionality, thus preventing reverse-engineering on the program even with the help of computing resources. The many advantages of code obfuscation, together with its low cost, makes it a popular technique. This paper proposes a novel methodology for source code obfuscation that can be used together with other traditional obfuscation techniques, making the code more robust against reverse engineering attacks. Three program complexity metrics are used to define three different single-objective combinatorial optimization versions of the problem, which are solved and analysed. Additionally, three multi-objective problems are defined, those considering each of the selected metrics together with the program execution time, in order to avoid strong obfuscations penalizing the performance. The goal of the defined problems is to find sequences of LLVM optimizations that lead to highly obfuscated versions of the original code. These transformations are applied to the back-end pseudo-assembly code (i.e., LLVM Intermediate Representation), thus avoiding any further optimizations by the compiler. Classical genetic algorithms (GAs) are used to solve the studied problems, namely a basic cellular GA for the single-objective problems and the popular NSGA-II for the multi-objective ones. The promising results show the potential of the proposed technique.
PB Oxford University
YR 2024
FD 2024-05-14
LK http://hdl.handle.net/10498/32296
UL http://hdl.handle.net/10498/32296
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 07-nov-2026