Comparison of Multi-objective Optimization Algorithms for the JShadObf JavaScript Obfuscator

With the advent of the Cloud Computing (CC) paradigm and the explosion of new Web Services proposed over the Internet (such as Google Office Apps, Dropbox or Doodle), the protection of the programs at the heart of these services becomes more and more crucial, especially for the companies making business on top of these services. The majority of these services are now using the JavaScript programming language to interact with the user as all modern web browsers - either on desktops, game consoles, tablets or smart phones - include JavaScript interpreters making it the most ubiquitous programming language in history. This context renew the interest of obfuscation techniques, i.e. to render a program "unintelligible" without altering its functionality. The objective is to prevent the reverse-engineering on the program for a certain period of time - an absolute protection by this way being unrealistic since stand-alone obfuscation for arbitrary programs has been proven impossible in 2001. In [11], we have presented JSHADOBF, an obfuscation framework based on evolutionary heuristics designed to optimize for a given input JavaScript program, the sequence of transformations that should be applied to the source code to improve its obfuscation capacity. Measuring this capacity is based on the combination of several metrics optimized simultaneously with Multi-Objective Evolutionary Algorithms (MOEAs). In this paper, we extend and complete the experiments made around JSHADOBF to analyze the impact of the underlying Multi-Objective Evolutionary Algorithms (MOEAs) algorithm onto the obfuscation process. In particular, we compare the performances of NSGA-II and MOEAD (two reference algorithms in the optimization domain) on top of JSHADOBF to first obfuscate a pedagogical program inherited from linear algebra, then one of the most popular and widely used JavaScript library: JQuery.