Side‐channel analysis (SCA) is a type of cryptanalytic attack that uses unintended ‘side‐channel’ leakage through the real‐world execution of the cryptographic algorithm to crack a secret key of an embedded system. These side‐channel errors can be discovered through tracking the energy usage of the device performing the technique, electromagnetic radiations while the encryption process, execution time, cache hits/misses, and others. Nowadays, deep learning‐based detection techniques are considered as emerging techniques that have been proposed for attack detection. Deep learning architectures have the ability to learn autonomously and concentrate on difficult features, in contrast to machine learning models. In light of these factors, the work's motive is thought to be the proposal of a deep learning‐based attack detection method. Many methods are used to decrease these assaults, however, the majority of them are inefficient and time‐demanding. In order to address these challenges, this study employs a novel deep learning‐based methodology. Pre‐processing, feature extraction, and SCA classification are the three stages of the approach proposed in this work. First, pre‐processing is used to remove unnecessary information and improve the quality of the input using data cleaning and min‐max normalization. The previously processed data are then fed as input into the proposed hybrid deep learning architecture. A Deep Residual Capsule Auto‐Encoder (DR_CAE) model is introduced in the proposed study. The deep residual neural network‐50 (DRNN‐50) is utilized to extract relevant features in this case, while the side channel analysis is done by using capsule auto‐encoder (CAE). The parameters of the proposed model are adjusted using the modified white shark optimization (MWSO) technique to improve its performance. In the results section, the proposed model is compared to various existing models in terms of accuracy, precision, recall, F‐measures, time, and so on. The proposed framework has an accuracy of 98.802%, F‐measures of 98.801%, kappa coefficient of 97.6%, the precision value of 98.81%, and recall value of 98.80%.

中文翻译：

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使用带有胶囊自动编码器网络的深度学习模型进行侧信道攻击检测

侧信道分析 (SCA) 是一种密码分析攻击，它通过现实世界中执行的加密算法使用意外的“侧信道”泄漏来破解嵌入式系统的密钥。这些旁道错误可以通过跟踪执行该技术的设备的能源使用情况、加密过程中的电磁辐射、执行时间、缓存命中/未命中等来发现。如今，基于深度学习的检测技术被认为是针对攻击检测而提出的新兴技术。与机器学习模型相比，深度学习架构能够自主学习并专注于困难的特征。鉴于这些因素，这项工作的动机被认为是提出一种基于深度学习的攻击检测方法。人们使用了许多方法来减少这些攻击，但大多数方法效率低下且耗时。为了应对这些挑战，本研究采用了一种新颖的基于深度学习的方法。预处理、特征提取和 SCA 分类是本工作中提出的方法的三个阶段。首先，预处理用于删除不必要的信息，并使用数据清理和最小最大标准化来提高输入的质量。然后，先前处理的数据将作为输入输入到所提出的混合深度学习架构中。本研究引入了深度残差胶囊自动编码器（DR_CAE）模型。在这种情况下，利用深度残差神经网络-50（DRNN-50）来提取相关特征，而侧通道分析则通过使用胶囊自动编码器（CAE）来完成。使用改进的白鲨优化（MWSO）技术调整所提出模型的参数以提高其性能。在结果部分，所提出的模型在准确度、精确度、召回率、F 测量、时间等方面与各种现有模型进行了比较。所提出的框架的准确率为98.802%，F-measures为98.801%，kappa系数为97.6%，精确度值为98.81%，召回率为98.80%。