Voronoi Based Multidimensional Parameter Optimization for Fault Injection Attacks

Fault injection attacks are used to overcome security mechanisms of embedded devices. While this can be done with low-cost equipment for simple targets and attack types, more sophisticated targets require a higher precision and better equipment. Precise parameters can also help to increase reliability and thus minimize the probability of damage, which is essential for forensic data extraction. In this work, we present a novel iterative method for finding suitable parameter combinations for fault injection attacks based on Voronoi tessellation, an algorithm for partitioning a space into cells based on the given input points. This method can reduce the number of parameter combinations to be tested before finding a successful combination, while preserving optimal candidate solutions. We show that the method can work with an arbitrary number of parameters and arbitrary result shapes. In addition, we show some algorithmic refinements that can help reduce computation time for high-dimensional parameter spaces. This allows not only the optimization of parameter-intensive attack types, but also the inclusion of non-essential parameters to further improve the reliability of the results. Simulation results show the potential of the proposed method, especially when it comes to minimizing the number of resets performed. Likewise, we discuss the problems encountered when applying this method to sensitive real devices and propose solutions to overcome them.