# Challenging Assumptions of Normality in AES s-Box Configurations under Side-Channel Analysis
## 3 Methods  
To adequately address the assumption of normality in side-channel analysis strategies, it is paramount to understand how data was collected. In Section 2, the evolution of side-channel analysis strategies was outlined, providing commentary on how such approaches could be applied to key recovery under AES encryption. Details of any underlying computational device were omitted to reduce the overhead of understanding SCA tooling. Going forward, the ChipWhisperer Lite (CWLite) build system, consisting of a multi-use capture unit and a target board [21], is utilized. An STM32F303 microcontroller occupies the target board, commonly referred to as the device under test (DUT). This device was selected for 10-bit precision with a sampling rate of 105 Mega samples per second (MS/s). These ADC capture capabilities exceed the TVLA sampling requirement of an 8-bit precision on measurements [13]. To accommodate the requirement for the number of samples, \(n\), to be at least greater than 5000 [13, 24,400 samples were gathered for each trace. A suitable AES implementation is the next requirement that must be satisfied. Tiny AES in C (Tiny-AES-c) is a well-known, widely used implementation of AES in C [22], with full CWLite integration across many DUTs, including the STM23F303. A suite of ChipWhisperer tools, such as the DPA ChipWhisperer tutorial [23], in tandem with Tiny-AES-c provides a framework for data collection. To add variability to this study, nine s-box configurations were utilized. The first of these s-boxes is included in Tiny-AES-C and will be referred to as the _Default_ s-box. The remaining eight s-boxes, referenced as S1 through S8, were sourced from [PERSON] et al. [24] and were selected due to their consistent measure of nonlinearity, defined by [PERSON] et al. [25], which is identical to the Default s-box. Maintaining  
Figure 2: An example TVLA output for fixed and random plaintext outputs when run through Tiny-AES-c with the S1 s-box. the same level of nonlinearity reduces the risk of SCA's more powerful statistical methods producing biased results relating to different levels of information-theoretic entropy [26]. For each s-box, an instance of Tiny-AES-C was created. This instance was then used to generate power consumption data based on the NIST TVLA guidelines, with approximately 500 traces being gathered for each of the two groups of data, with a total of 1000 traces being collected. The variance in group sizes originates from the fact that the plaintext generation is pseudorandom. From experimental testing, the number of traces in a group varied by at most 2.5%. Two groups of data are required for TVLA; one with a known plaintext, with the associated power traces being referred to hereafter as the _fixed data_, and the other group consisting of power traces associated with random plaintexts, which will be addressed as _random data_. It is important to note here that the key used with both groups remains fixed throughout the data collection process. Using the CWLite system, twenty sets of fixed and random data were collected for each of the nine s-boxes. Before any further analysis was performed, master traces were computed for each pair of data and compared against their corresponding counterpart. No discernible outliers were detected within each of the twenty collections for a given s-box, signifying no issues with the data collection process. For the remainder of this analysis and without the loss of generality, the thirteenth collection of data for each of the nine s-boxes was used. Unless otherwise noted, all tests were run on the full sets of traces and not their corresponding master traces. As outlined in Section 2, the first step is establishing a baseline analysis using traditional TVLA. This analysis focuses on applying [PERSON]'s \(t\)-test, a statistical measure used in the seminal TVLA paper [13].