# Challenging Assumptions of Normality in AES s-Box Configurations under Side-Channel Analysis
## 2 Related Works
[10], even when random process interrupts are implemented in a device to prevent against information leakage through power-based side-channel analysis, DPA can still be applied to gain information about the device. As such, DPA's usage of a statistic added resilience against countermeasures intended for side-channel analysis. However, it was limited in scope by its reliance on splitting the power traces based on their sensitivity of a bit at a single point in time. For example, a properly instrumented device may have an AES-key fully recovered using DPA. This attack-vector involves gathering an appropriate corpus of data and then examining each of the possible key-guesses for a difference in behavior using DPA. Once this operation is complete, the computed difference is evaluated for the largest difference between the two trace groups. For a visual representation of this process when applied to the AES algorithm, see Figure 1b. Whereas DPA focuses on the significance of the presence of a single bit at a single fixed time, the next major advancement in side-channel analysis was motivated by the desire to exhaustively examine the contribution of each time step in a power trace. A new form of side-channel analysis, known as Correlation Power Analysis (CPA), was coined by [PERSON] et al. [7]. Taking inspiration from DPA, Pearson's Correlation Coefficient was calculated by utilizing pairs consisting of a Hamming Weight, the number of 1's in a binary representation, relevant to plaintext data and a set of collected power traces. CPA diverges from the DPA's dependence on the mean power traces of two data groups. This marked the first time that side-channel analysis was heading towards more statistically backed conclusions to determine where significant differences occur, improving the exploitation of leakage. To be more precise, [PERSON] et al. [7] concluded that while DPA can fail without knowledge of the underlying implementation of the cryptographic device, CPA can infer information relevant to the implementation. It is able to do all of this while requiring less power trace samples in comparison to DPA. However, [PERSON] et al. also stated that CPA is vulnerable to the same countermeasures as DPA, since both procedures depend upon \"sidechannel observability\". While CPA was to be a step towards applying statistically focused methods to side-channel analysis, [PERSON]'s Correlation Coefficient should only be used when assuming the normality of the samples, and when there exists a linear relationship between them [11]. Although it was initially used as an attack against AES, [PERSON] et al. [12] demonstrate that CPA can be applied to the lightweight block cipher algorithm LiCi, but the assumptions from utilizing [PERSON]'s Correlation Coefficient are still made, even on a different encryption scheme. The most recent noteworthy expansion to general power-based side-channel analysis was proposed by [PERSON] et al. [13]. This development focused on detecting whether a device has power-based leakage directly in contrast to SPA, DPA, or CPA, all of which focus on exploiting possible side-channels. The novel methodology has since been commonly referred to as Test Vector Leakage Assessment (TVLA). This methodology acts as a successor to other attacks such as CPA while focusing on leakage detection rather than leakage exploitation. Where TVLA steps into new territory is with the separation methodology. To properly apply TVLA, two unique sets of power traces are generated, each of which have a meaningful, data-dependent difference. For example, when assessing whether an AES implementation exhibits power-based leakage, the two groups of data differ based on whether the plaintext provided fits a fixed or random pattern. A NIST specification for TVLA [13] was produced to ensure that the testing parameters are consistent, allowing for a rigid, verifiable procedure to be produced. When collecting samples for TVLA, it is imperative that both groups of data are sampled during a single experimental run and not two separate experiments.