Diversity Algorithms for Laser Fault Injection

Before third-party evaluation and certification, manufacturers often conduct internal security evaluations on secure hardware devices, including fault injection (FI). Within this process, FI aims to identify parameter combinations that reveal device vulnerabilities. The impracticality of conducting an exhaustive search over FI parameters has prompted the development of advanced and guided algorithms. However, these proposed methods often focus on a specific, critical region, which is beneficial for attack scenarios requiring a single optimal FI parameter combination. In this work, we introduce two novel metrics that align better with the goal of identifying multiple optima. These metrics consider the number of unique vulnerable locations and clusters (regions). Furthermore, we present two methods promoting diversity in tested parameter combinations - Grid Memetic Algorithm (GridMA) and Evolution Strategy (ES). Our findings reveal that these diversity methods, though identifying fewer vulnerabilities overall than the Memetic Algorithm (MA), still outperform Random Search (RS), identifying at least \({\approx }8{\times }\) more vulnerabilities. Using our novel metrics, we observe that the number of distinct vulnerable locations is similar across all three evolutionary algorithms, with \({\approx }30\%\) increase over RS. Importantly, ES and GridMA prove superior in discovering multiple vulnerable regions, with ES identifying \({\approx }55\%\) more clusters than the worst-performing MA.