Numerous fault models with distinct characteristics and effects have been developed. The costs, repeatability, and practicability of these models should be assessed. Moreover, there must be effective ways to use the injected fault to retrieve the secret key, particularly if the implementation includes any countermeasures. In this paper, we introduce a new fault analysis called “linked fault analysis” (LFA), a more powerful technique than other well-known fault attacks against implementations of symmetric primitives, especially in software implementations. For known fault analysis, the basis for the fault model is either the bias over the faulty value or the relationship between the correct value and the faulty one. In the LFA, however, a single fault involves two intermediate values. The faulty target variable,$u'$, is linked to a second variable,$v$, such that a particular relation holds:$u'=l(v)$. LFA lets the attacker perform fault attacks without the input control, using far fewer data than previously introduced fault attacks in the same class. We show the utilization of LFA in the presence or absence of typical redundancy-based countermeasures by introducing “Linked Differential Fault Analysis” (LDFA) and “Linked Ineffective Fault Analysis” (LIFA). We also demonstrate that, under specific circumstances, LFA is still effective even when masking protections are in place. We have performed our attacks against the public implementation of AES and PRESENT in ATMEGA328p to show the feasibility of LFA in the real world. The practical results and simulations validate our theoretical models as well.

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.