Towards Better Program Obfuscation: Optimization via Language Models
Han Liu. ICSE 2016
As a common practice in software development, program obfuscation aims at deterring reverse engineering and malicious attacks on released source or binary code. Owning ample obfuscation techniques, we have relatively little knowledge on how to most effectively use them. The biggest challenge lies in identifying the most useful combination of these techniques. We propose a unified framework to automatically generate and optimize obfuscation based on an obscurity language model and a Monte Carlo Markov Chain (MCMC) based search algorithm. We further instantiate it for JavaScript programs and developed the Closure tool. Compared to the well-known Google Closure Compiler, Closure outperforms its default setting by 26%. For programs which have already been well obfuscated, Closure can still outperform by 22%.
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