# Side-Channel Power Analysis Based on SA-SVM
## 1 Introduction  
When cryptographic algorithms are performed on electrical devices, the hardware circuit will leak out relevant physical information such as time [1], electromagnetic radiation [2], power consumption, optics [3], acoustics [4], etc. Unlike traditional cryptanalysis, side-channel analysis (SCA) avoids analyzing the cryptographic algorithm itself. SCA usually splits the master key into several subkeys, and the leaked physical information of these subkeys can be captured by the attacker. The attacker combines the relevant knowledge to recover the subkeys and eventually the master key. [PERSON] et al. [5] suggested a power consumption attack for the first time in the late 1990s. It is a branch of side-channel attacks that targets devices by measuring power consumption. [PERSON] proposed that the classic differential power analysis (DPA) had successfully cracked the DES algorithm key. They found that there was a correlation between power consumption and data when the device was being encrypted. Additionally, the relation contains encrypted device key data that can be used to crack the key. By analyzing the power consumption of a device during encryption or decryption, it is possible to deduce the key used. In order to carry out this type of attack, a computer uses an encryption device and inputs a set of known plaintexts into the device for encryption. As the device performs encryption, an oscilloscope measures power consumption, thus obtaining power traces. Following this approach, more power analysis attack methods were developed, which can be broadly classified as profiling attacks and nonprofiling attacks. Nonprofiling attacks include mutual information analysis (MIA) [6] and collision attacks (CAs) [7], in addition to the commonly used DPA and correlation power analysis (CPA) [8]. Profilingattacks include template attacks (TAs) [9] and machine-learning-based SCA (MLSCA), such as multilayer perceptron (MLP) [10], random forests (RFs) [11; 12], k-nearest neighbors (KNNs) [13], convolutional neural networks (CNNs) [14; 15], and support vector machines (SVMs) [16; 17; 18; 19; 20]. Nonprofiling attacks are simple but vulnerable to environmental interference. In contrast, profiling attack techniques are more resilient to environmental noise because they require full control of a device that is identical to the target device. The attacker uses the owned device to create a side-channel leakage model based on a large number of samples, which allows for easier key cracking on the target device using this attack. [PERSON] et al. [16] applied the LS-SVM model to power consumption analysis attacks for the first time. The findings demonstrated that the parameter selection of machine learning technology has a substantial effect on categorization performance. [PERSON] and [PERSON] et al. [17] extended the bit model to a Hamming weight (HW) model by assuming intermediate values to classify power consumption profiles and decreased spatial complexity, demonstrating that SVM-based attacks beat conventional template attacks in high-noise situations. [PERSON] et al. [18] used an SVM based on wavelet kernels to recover the offset values and keys of a masked AES algorithm, showing that wavelet kernel vector machines beat Gaussian kernel vector machines. [PERSON] and [PERSON] et al. [19] used the SMOTE algorithm to address the problem of unbalanced data during SVM training caused by the HW model. For feature extraction and selection of power traces, [PERSON] et al. [20] combined variational mode decomposition with the Gram-Schmidt method to select eigenvalues. The method is capable of extracting features that retain the most important information from the power trace while reducing noise, which are then used for SVM classification. The above contributions show that SVMs outperform other MLSCA methods. However, the problem of parameter selection in SVMs still remains unsolved by their methods. Heuristic algorithms are widely used in optimization problems.