Algorithm Diversity for Resilient Systems
This work addresses resilience in systems for security applications by proposing a novel approach to algorithm diversity, though it appears incremental in combining existing concepts.
The paper tackles the problem of increasing system resilience by introducing algorithm diversity, using high-level invariants and incrementalization to create variants, and demonstrates that executing multiple variants in parallel with synchronized execution in DistAlgo provides greater resilience than single variants.
Diversity can significantly increase the resilience of systems, by reducing the prevalence of shared vulnerabilities and making vulnerabilities harder to exploit. Work on software diversity for security typically creates variants of a program using low-level code transformations. This paper is the first to study algorithm diversity for resilience. We first describe how a method based on high-level invariants and systematic incrementalization can be used to create algorithm variants. Executing multiple variants in parallel and comparing their outputs provides greater resilience than executing one variant. To prevent different parallel schedules from causing variants' behaviors to diverge, we present a synchronized execution algorithm for DistAlgo, an extension of Python for high-level, precise, executable specifications of distributed algorithms. We propose static and dynamic metrics for measuring diversity. An experimental evaluation of algorithm diversity combined with implementation-level diversity for several sequential algorithms and distributed algorithms shows the benefits of algorithm diversity.