Statistical Effective Fault Attacks: The Other Side of the Coin

Abstract

The introduction of Statistical Ineffective Fault Attacks (SIFA) has led to a renewed interest in fault attacks. SIFA requires minimal knowledge of the concrete implementation and is effective even in the presence of common fault or power analysis countermeasures. However, further investigations reveal that undesired and frequent ineffective events, which we refer to as the noise phenomenon, are the bottleneck of SIFA that can considerably diminish its strength. This includes noise associated with the attack's setup and caused by the countermeasures utilized in the implementation. This research aims to address this significant drawback. We present two novel statistical fault attack variants that are far more successful in dealing with these noisy conditions. The first variant is the Statistical Effective Fault Attack (SEFA), which exploits the non-uniform distribution of intermediate variables in circumstances when the induced faults are effective. The idea behind the second proposed method, dubbed Statistical Hybrid Fault Attacks (SHFA), is to take advantage of the biased distributions of both effective and ineffective cases simultaneously. Our experimental results in various case studies, including noise-free and noisy setups, back up our reasoning that SEFA surpasses SIFA in several instances and that SHFA outperforms both or is at least as efficient as the best of them. For example, in the case of a 4-bits random-AND fault injected into the AES with a 35% missed fault rate, utilizing SEFA reduces the number of needed ciphertexts by 50%. In the same case study, SHFA can yield 10% and 55% reductions compared to SEFA and SIFA.