Algorithmic Diversity for Software Security

Software diversity protects against a modern-day exploits such as code-reuse attacks. When an attacker designs a code-reuse attack on an example executable, it relies on replicating the target environment. With software diversity, the attacker cannot reliably replicate their target. This is a security benefit which can be applied to massive-scale software distribution. When applied to large-scale communities, an invested attacker may perform analysis of samples to improve the chances of a successful attack [1]. We present a general NOP-insertion algorithm which can be expanded and customized for security, performance, or other costs. We demonstrate an improvement in security so that a code-reuse attack based on any one variant has minimal chances of success on another and analyse the costs of this method. Alternately, the variants may be customized to meet performance or memory overhead constraints. Deterministic diversification allows for the flexibility to balance these needs in a way that doesn’t exist in a random online method.