## 2 Related Work  
There has been a great interest in using machine learning for side channel analysis in the last decade. The growing number of conference papers and journal articles have required systemization, which has been reflected by multiple reviews and state-of-the-art articles published throughout the years. The first to comprehensively study applying ML techniques in the context of SCA were [PERSON], [PERSON], and colleagues [19]. In their article, they compared the accuracy of the LS-SVM algorithm with a Template Attack (TA) using the power trace of an AES software implementation. They examined different parameter sets, numbers of traces used, and preprocessing methods and concluded that properly tuned ML algorithms outperformed the TA. In 2014, [PERSON] and [PERSON] [20] prepared an overview of ML-based SCA attacks on various cryptosystems including one ECC [21] and one RSA [22]. Later, in 2016, in a very extensive review of secure architectures with an emphasis on physical attacks by [PERSON], [PERSON], and colleagues [23], the authors stated that the usage of ML for SCA is still in the early stages and comparisons of such methods are not purposeful due to differences in the ML algorithms used and target cryptosystem implementations. Nevertheless, the authors cited a handful of papers categorizing them based on the ML algorithm used. One year later, the book \"Hardware Security and Trust\" was published with a chapter from [PERSON], [PERSON], and [PERSON] [24] summarizing the SCA attacks against ECC, including ML methods. In 2018 [PERSON], [PERSON], and colleagues [25] presented the first comprehensive study of deep learning (DL) methods used in SCA and introduced an open-source dataset consisting of electromagnetic emanation (EM) traces of a software, masked AES implementation, as a benchmark for future researchers interested in DL. In recent years, the usage of ML for SCA against public-key cryptography has only been mentioned in [26; 27; 28]. The only extensive summary can be found in the state-of-the-art chapter of [PERSON]'s doctoral thesis [29], where he was able to list 12 publications concerning the usage of ML in SCA against ECC and RSA. As can be observed, the side channel analysis of public-key cryptography implementations has always been less represented in the literature. One of the reasons is the small number of open-source datasets. According to [30], there are 11 commonly used open-source datasets for AES and only four for ECC [18; 31; 32]. Despite the lack of open-source datasets, there are many proprietary datasets used, wherein the authors only describe the used implementations but do not publish the traces. This prevents comprehensive comparisons but still contributes to the progression of the field. The high accessibility of datasets with AES implementation has always spurred interest among researchers. The following AES-dedicated summaries were published in 2019 [33], 2020 [34], and 2021 [30; 35]. The growing number of publications regarding AES implementations has led to situations when researchers even forget to mention what the target cryptosystem is, assuming, by default, some AES implementation. Moreover, conducting SCA attacks against AES allows leveraging multiple power traces associated with the same key while EC-based cryptosystems give much less information to the attacker. The key is ephemeral; it is used only once due to either scalar randomization or nonce randomization between consecutive Elliptic Curve Scalar Multiplications (ECSMs). It is harder to create a profile of a device or use methods that require a larger corpus of data, like DL. Nevertheless, there are still some works dedicated to SCA attacks against ECC. It is not our purpose to list them all in this article, but we will focus on a particular subgroup of attacks. First and foremost, we are interested in horizontal attacks.