## 2 Related Work
The goal of the next step is to refine the list of POIs to reduce them even further and increase the likelihood of choosing POIs associated with the leakage. The authors ran a \(t\)-test with two groups of  
Figure 1: Depiction of (**a**) Feed-Forward Neural Network architecture and (**b**) autoencoder architecture. traces, those where the corresponding bit was assumed to be \"one\" and those where it was assumed to be \"zero\". Samples with the highest t-score were chosen as the next round's POIs and the key was recovered based on the refined list. The subject of eliminating POIs not containing leakage was used in context of ECC, in [18], by [PERSON] et al. The authors proposed an unsupervised, non-profiled iterative SCA framework using Cluster Leakage Assessment for initial key candidates' assumption and a Convolutional Neural Network with dropout as a final distinguisher and for the elimination of POIs. The choice of POIs was also addressed by [PERSON] et al. in [PERSON] et al. (2018). Some points detected as POIs are not key- but data-dependent and simply add noise to the analysis. The authors assumed that there are usually more than one key-dependent POIs in a power trace and that those points should correlate with each other, while multiple data-dependent points will not. They ran a pairwise covariance computation between all pairs of POIs and ended up with a covariance matrix. It was known that samples close to each other would be correlated; therefore, pairs of samples further away from the covariance diagonal, which had a high magnitude of covariance, were considered as key-dependent; hence, these were chosen as POIs for further analysis. The covariance calculation increased the correctness of multivariate k-means clustering, leading to a more successful attack against an FPGA implementation of ECSM. It did, however, fail in case of an attack against a software implementation, probably due to misalignment. The choice of POIs can also be driven by the knowledge of the underlying algorithm, and so, in [PERSON] et al. (2017), [PERSON] et al. focused on the key identification phase of each ECSM iteration. The identification phase was defined as the time when the key bit was stored in a temporal variable/register. The authors attacked a hardware and software implementation of ECSM, always considering only the beginning of each iteration using the k-means, fuzzy k-means, and expectation maximization algorithms, and recovered the scalar successfully. The ECSM iterations were aligned as part of the preprocessing. In Table 1, we summarize all related publications that we could have found and which used machine learning in the context of the Side Channel Analysis of Elliptic Curve Scalar Multiplication (or when the authors stated that the analysis could be used for ECSM). All authors assumed no knowledge about the processed key and all of the listed works used machine learning in either the preprocessing step or as a final classifier for ECSM iterations, i.e., to tell which iterations processed a \"one\" and which processed a \"zero\". For software (SW) implementations, we also report the software library used. Moreover, we provide details about the platforms running the implementation and the type of captured trace (PT--Power Trace, EMT--Electromagnetic Trace). The last entry is dedicated to our conference publication [17] and this extended work. The abbreviations are listed below Table 1 for readability. Our work, similar to the non-profiled framework idea presented in [18], consists of applying a horizontal attack and subsequently training a neural network with noisy labels obtained from the attack. The idea can be extended to leverage any unsupervised attack presented in Table 1 to subsequently train a supervised classifier with high regularization to improve the success rate. The main difference between our work and [18] is that we attacked hardware implementations, not software implementations.