# Key Schedule against Template Attack-Based Simple Power Analysis on a Single Target
## 1 Introduction  
Template attacks were suggested by [PERSON] et al. [1] as one of the strongest side-channel attacks. In general, a template attack consists of two phases: profiling and extraction. To conduct template attacks, in the profiling phase, the attacker has to find the point of interest (PoI). Based on this information, templates are built using statistical tools or machine learning (ML). The significant aspect is that an adversary must have the knowledge of the secret information, such as the correct key, all instructions, and device specification. Afterward, based on its setting, the extraction phase is conducted to retrieve the correct key, with a different device. To enhance the performance of template attacks, many contributions have been proposed. In order to find appropriate Pols, statistical tools such as sum of squared pairwise T-difference (SOST) [2], normalized inter-class variance (NICV) [2], difference of mean (DOM) [3], sum of squared difference (SOSD) [2], signal to noise ratio (SNR) [4], mutual information analysis (MIA) [5], Komogorov-Smirnovanalysis (KSA) [6], and variance (VAR) [7] have been suggested. Among these, the SOST tool is the most commonly employed to find Pols. In contrast, many researchers have struggled to construct an accurate leakage model. In terms of the (multivariate) Gaussian distribution model, there are some variants for covariance to improve the accuracy of prediction model. In particular, some proposals show remarkable results when applying the average to traces. Moreover, ML schemes, such as random forest, support vector machine, and self-organizing map [8; 9], can be applied to build templates. Deep learning, convolutional neural networks, and multi-layer perception are also used to mount profiling attacks, based on artificial intelligence priciples. Most template attacks including the profiling attacks are applied to the internal of encryption processes such as S-boxes. Therefore, some studies [10; 11] try to overcome the masking countermeasures to apply profiling attacks. To return to key schedule sight, some papers [7; 12; 13; 14] are applied to profiling attacks to extract round keys. **Related Works** Sometimes, the key schedule can be the main target of side-channel analysis based on the profiling attack because the master key cannot usually be changed in attack device. Some papers [7; 12; 13; 14] show that side-channel leakage can be extracted to acquire the master key or round key, when some operations are computed in a key schedule. The brief description is as follows. * **[PERSON] et al. [12]** In software implementation of the smart card, they described that it is possible to recover the key value after producing some templates for each Hamming weight (HW) value of the key-dependent region. It is required to use a specific algorithm to distinguish the HW value of a key after collecting the different cryptographic key and fixed data, but they did not show the experimental results. That is, some ideas for side-channel analysis with respect to key schedules were just suggested. * **[PERSON] et al. [13]** Their main target was the exclusive or (XOR) operation of the round key when the cryptographic key is fixed. To obtain the round key, they first divided each round in a key schedule operation to observe the difference of the key bit. Subsequently, it was compared between each rounds. They confirmed that the difference which was found depends on the key bit when XOR operation is computed in each round. * **[PERSON] [14]** He proposed two strategies for a key schedule. In the first strategy, he learned the full leakages for round keys on another target. Subsequently, the round keys could be extracted on an attack target. In the second strategy, he referred that an adversary could learn the properties of HW for processing known values, such as plaintext/ciphertext on XOR operation, in the case that an adversary only had access to an attack target. However, he did not show the experimental results for the two strategies. * **[PERSON] et al.