Fault-Simulation-Based Flip-Flop Classification for Reverse Engineering

This work outlines a crucial step in gate-level netlist reverse engineering: classifying control and data flip-flops (FFs) to discern control logic and data paths. Existing methods rely mainly on structural characteristics, which can have disavantages. Our work introduces a novel approach that classifies FFs based on observed characteristics after fault insertion and propagation. We develop three new classification methods for block cipher implementations, emphasizing their significance in system security. However, we also explore the approach's applicability to other design types. We apply the approach on AES implementations using an automatic fault simulation framework, which shows perfect results for most classifications.