Playing With Blocks: Toward Re-Usable Deep Learning Models for Side-Channel Profiled Attacks

Paguada, Servio; Batina, Lejla; Buhan, Ileana; Armendariz, Igor

doi:10.1109/tifs.2022.3196273

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…The linear layers known as convolutional layers (CONV) share their weights in all directions. Since CNNs were initially developed for images, Figure 1 presents an alternative representation from the most common one, which organises tier interactions resembling 3D-patterns (depth, height and weight) [13]. Similar to side-channel traces, Figure 1 depicts a 2D convolutional neural network, or CNN, tuned for 1D data.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Design of Time-Delay Convolutional Neural Networks(TDCNN) Model for Feature Extraction for Side-Channel Attacks

Abbas Ahmed,

Kamrul Hasan,

Azman Mohd Noah

et al. 2024

IJCDS

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This work explores a novel method of SCA profiling to address compatibility problems and strengthen Deep Learning (DL) models. Convolutional Neural Networks are proposed in this research as a countermeasure to misalignment-focused countermeasures. "Time-Delay Convolutional Neural Networks" (TDCNN) is more accurate than "Convolutional Neural Network," yet it's still acceptable. It's true that TDCNNs are neural networks based on convolution learned on single spatial information, just as side-channel tracings. However, given to recent surge in popularity of CNNs, particularly from the year 2012 when CNN framework ("AlexNet") achieved Image Net Large Scale Visual Recognition Competition which is a notable image detection competition, a novel TDCNN has been termed out in DL literature. Currently, it needs to employ the characteristics related to CNN design, including declaring that one input feature equals 1 for instance, to establish a TDCNN in the most widely used DL libraries.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Design of Time-Delay Convolutional Neural Networks(TDCNN) Model for Feature Extraction for Side-Channel Attacks

Abbas Ahmed,

Kamrul Hasan,

Azman Mohd Noah

et al. 2024

IJCDS

View full text Add to dashboard Cite

show abstract

“…To do an emotional orientation analysis means to conduct an analysis of the text, which involves the feelings and personal experiences of the customers. Emotion dictionary ( Cochrane et al, 2022 ; Jang, Choi & Kim, 2022 ), machine learning ( Kalaiarasi & Maheswari, 2021 ; Vadhnani & Singh, 2022 ; Yalsavar et al, 2022 ), and deep learning ( Strawn, 2022 ; Stultz, 2021 ; Paguada et al, 2022 ) are some of the technologies that are frequently utilized in the numerous research projects that are conducted on text emotion analysis both in the United States and internationally. Alswaidan & Menai (2020) examines and compares four distinct approaches to recognizing the emotions conveyed in written text: a rule-based approach, a classical learning approach, a depth learning approach, and a hybrid approach.…”

Section: Introductionmentioning

confidence: 99%

A consumer emotion analysis system based on support vector regression model

Huo

2023

PeerJ Computer Science

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The effective means to stimulate economic growth is to enhance consumers’ consumption capacity. Because many consumers have different consumption habits, they will pay different attention to products. Even the same consumer will have different shopping experiences when buying the same product at different times. By mining the online comments of consumers on the online fitness platform, we can find the characteristics of fitness projects that consumers care about. Analyzing consumers’ emotional tendencies towards the characteristics of fitness programs will help online fitness platforms adjust the quality and service direction of fitness programs in a timely manner. At the same time, it can also provide purchase advice and suggestions for other consumers. Based on this goal, this study uses an optimized support vector regression (SVR) model to build a consumer sentiment analysis system, so as to predict the consumer’s willingness to pay. The optimized SVR model uses the region convolution neural network (RCNN) to extract features from the dataset, and uses feature data to train the SVR model. The experimental results show that the SVR model optimized by RCNN is more accurate. The improvement of the accuracy of consumer sentiment analysis can accurately help businesses promote and publicize, and increase sales. On the other hand, the increase in the accuracy of emotion analysis can also help users quickly locate their favorite fitness projects, saving browsing time. To sum up, the emotional analysis system for consumers in this paper has good practical value.

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Improving Side-channel Leakage Assessment Using Pre-silicon Leakage Models

Shanmugam

Schaumont

2023

Lecture Notes in Computer Science

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Playing With Blocks: Toward Re-Usable Deep Learning Models for Side-Channel Profiled Attacks

Cited by 5 publications

References 24 publications

Design of Time-Delay Convolutional Neural Networks(TDCNN) Model for Feature Extraction for Side-Channel Attacks

Design of Time-Delay Convolutional Neural Networks(TDCNN) Model for Feature Extraction for Side-Channel Attacks

A consumer emotion analysis system based on support vector regression model

Improving Side-channel Leakage Assessment Using Pre-silicon Leakage Models

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