Tailored source code transformations to synthesize computationally diverse program variants

The predictability of program execution provides attackers a rich source of knowledge who can exploit it to spy or remotely control the program. Moving target defense ad-dresses this issue by constantly switching between many di-verse variants of a program, which reduces the certainty that an attacker can have about the program execution. The ef-fectiveness of this approach relies on the availability of a large number of software variants that exhibit different ex-ecutions. However, current approaches rely on the natural diversity provided by off-the-shelf components, which is very limited. In this paper, we explore the automatic synthe-sis of large sets of program variants, called sosies. Sosies provide the same expected functionality as the original pro-gram, while exhibiting different executions. They are said to be computationally diverse. This work addresses two objectives: comparing different transformations for increasing the likelihood of sosie synthe-sis (densifying the search space for sosies); demonstrating computation diversity in synthesized sosies. We synthesized 30 184 sosies in total, for 9 large, real-world, open source ap-plications. For all these programs we identified one type of program analysis that systematically increases the density of sosies; we measured computation diversity for sosies of 3 programs and found diversity in method calls or data in more than 40 % of sosies. This is a step towards controlled massive unpredictability of software. 1