# Power-Based Side-Channel Attacks on Program Control Flow with Machine Learning Models
## 2 Related Works
### SCA Backgrounds
Instead of collecting and analyzing arbitrary power traces to determine the leakage of information Test Vector Leakage Assessment (TVLA) can be used [17]. TVLA involves using statistical tests to determine if there is significant evidence to determine if the device had any leakage. [PERSON] et al. utilized the [PERSON]'s \(t\)-test to determine if there is a significant difference between two groups. The hypothesis tested whether the gathered traces are truly random or if there is leakage present within traces. While this methodology can be beneficial and has been used recently to show side-channel vulnerabilities in some of the NIST lightweight cryptography round 2 candidate s-boxes [18,19], TVLA can have issues demonstrated by relatively high rates of false negatives or false positives, even when using different tests such as the Pearson \(\chi^{2}\) test [20]. Signal Processing also can be used for SCA both as an attack vector and for defensive countermeasures, as mentioned by [PERSON] et al. [21]. In addition, [PERSON] et al. also demonstrates how three different signal processing techniques could be applied to SCA, which would allow for more ways to implement it. The work also defends using signal processing to mitigate SCA. There is also a precedent for AutoML being utilized for signal processing; an example uses the AutoML procedure of acquiring correct hyperparameters for deep learning to classify Electroencephalography (EEG) signals [22]. The work also claims that EEG signal classification is complex enough that machine learning techniques such as deep learning are \"appropriate to find the best solutions\". The hyperparameter tuning and deep learning from AutoML were stated to have less overfitting, and thus, yielded more optimized models for classifying the EEG signals. Time-series forecasting, although prominently implemented with traditional machine learning methods, has significantly less implementation within AutoML toolchains, being cited as \"still in the development stage\" [23]. It is demonstrated in their review article that there are gaps in traditional time-series forecasting with machine learning in terms of reapplying AutoML to time-series methods that used traditional machine learning [23]. The article showcases that there are research avenues, such as deep learning or neural architecture search (NAS), to implement AutoML for time-series analysis. AutoML has been implemented in analyzing time-series data, as experimented previously in comparison to rigorous, hand-crafted machine learning models [24]. In this paper, its authors conclude that in short-term models for time-series predictions, AutoML does not outperform traditional methods of using Machine Learning by manually tuning hyperparameters and preprocessing data. However, suggestions for time-series analysis with AutoML are: validation on the selection strategy with statistical significance tests; adding permutation strategies; and considering the cost of using AutoML with the benefit of its implementation. Additional implementation of AutoML utilize the process of NAS to efficiently search for an effective neural network architecture for utilization on time-series data [25]. It mentions that AutoML significantly improved the performance of searching a data-augmented time-series neural network architecture. The significance of NAS's performance boost was such that it outperformed other \"best\" statistical models. In terms of future possible improvements, the authors mention that further performance enhancements on the data augmentation could come from using other deep learning models such as GRU-AE and ConvLSTM. One of the implementations of machine learning for side-channel analysis is named Deep Learning-based Side-Channel Analysis (DL-SCA) [26]. DL-SCA is a new area of research, which is signified by a large increase of papers on this topic. An advantage of DL-SCA includes more powerful analysis by taking up to a factor of five times less data to break through targets with countermeasures as compared to template attacks.