Push for More: On Comparison of Data Augmentation and SMOTE with Optimised Deep Learning Architecture for Side-Channel

Won, Yoo-Seung; Jap, Dirmanto; Bhasin, Shivam

doi:10.1007/978-3-030-65299-9_18

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…While the results of the two datasets can obviously not be compared directly, we can nevertheless extract some common lines. First of all, lots of the state-of-the-art methods have been evaluated on this fixed key dataset and, among the best results obtained for the different settings, we can cite the ones by Wu et al [27], Zaid et al [28], Won et al [25] and Wouters et al [26]. As a comparison, these results are significantly better than the ones obtained with the original architecture proposed by Benadjila et al in [2].…”

Section: Comparison With Other Methods Used In State-of-the-artmentioning

confidence: 99%

Improving Deep Learning Networks for Profiled Side-channel Analysis Using Performance Improvement Techniques

Robissout

Bossuet

Habrard

et al. 2021

J. Emerg. Technol. Comput. Syst.

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The use of deep learning techniques to perform side-channel analysis attracted the attention of many researchers as they obtained good performances with them. Unfortunately, the understanding of the neural networks used to perform side-channel attacks is not very advanced yet. In this article, we propose to contribute to this direction by studying the impact of some particular deep learning techniques for tackling side-channel attack problems. More precisely, we propose to focus on three existing techniques: batch normalization, dropout, and weight decay, not yet used in side-channel context. By combining adequately these techniques for our problem, we show that it is possible to improve the attack performance, i.e., the number of traces needed to recover the secret, by more than 55%. Additionally, they allow us to have a gain of more than 34% in terms of training time. We also show that an architecture trained with such techniques is able to perform attacks efficiently even in the context of desynchronized traces.

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Section: Comparison With Other Methods Used In State-of-the-artmentioning

confidence: 99%

Improving Deep Learning Networks for Profiled Side-channel Analysis Using Performance Improvement Techniques

Robissout

Bossuet

Habrard

et al. 2021

J. Emerg. Technol. Comput. Syst.

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“…In addressing the challenge of class imbalance in datasets, the permutation technique [88][89][90] emerges as a novel data augmentation method, which is particularly effective for sequential or time-series data. At its core, the permutation technique involves dividing a signal into multiple non-overlapping segments and then rearranging these segments in various orders to generate new samples.…”

Section: Data Augmentationmentioning

confidence: 99%

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

Almujally,

Khan,

Al Mudawi

et al. 2024

Sensors

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The domain of human locomotion identification through smartphone sensors is witnessing rapid expansion within the realm of research. This domain boasts significant potential across various sectors, including healthcare, sports, security systems, home automation, and real-time location tracking. Despite the considerable volume of existing research, the greater portion of it has primarily concentrated on locomotion activities. Comparatively less emphasis has been placed on the recognition of human localization patterns. In the current study, we introduce a system by facilitating the recognition of both human physical and location-based patterns. This system utilizes the capabilities of smartphone sensors to achieve its objectives. Our goal is to develop a system that can accurately identify different human physical and localization activities, such as walking, running, jumping, indoor, and outdoor activities. To achieve this, we perform preprocessing on the raw sensor data using a Butterworth filter for inertial sensors and a Median Filter for Global Positioning System (GPS) and then applying Hamming windowing techniques to segment the filtered data. We then extract features from the raw inertial and GPS sensors and select relevant features using the variance threshold feature selection method. The extrasensory dataset exhibits an imbalanced number of samples for certain activities. To address this issue, the permutation-based data augmentation technique is employed. The augmented features are optimized using the Yeo–Johnson power transformation algorithm before being sent to a multi-layer perceptron for classification. We evaluate our system using the K-fold cross-validation technique. The datasets used in this study are the Extrasensory and Sussex Huawei Locomotion (SHL), which contain both physical and localization activities. Our experiments demonstrate that our system achieves high accuracy with 96% and 94% over Extrasensory and SHL in physical activities and 94% and 91% over Extrasensory and SHL in the location-based activities, outperforming previous state-of-the-art methods in recognizing both types of activities.

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“…There are many variants of SMOTE that have been successfully applied to various application domains such as bioinformatics, video surveillance, fault detection or high dimensional gene expression data sets [47], [49], [50]. There are many variants of SMOTE such as regular SMOTE, Borderline-SMOTE, SVM-SMOTE and KMeans-SMOTE [51].…”

Section: Related Work On Class Imbalance Problems a Synthetic Minorit...mentioning

confidence: 99%

SMOTified-GAN for Class Imbalanced Pattern Classification Problems

2022

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Class imbalance in a dataset is a major problem for classifiers that results in poor prediction with a high true positive rate (TPR) but a low true negative rate (TNR) for a majority positive training dataset. Generally, the pre-processing technique of oversampling of minority class(es) are used to overcome this deficiency. Our focus is on using the hybridization of Generative Adversarial Network (GAN) and Synthetic Minority Over-Sampling Technique (SMOTE) to address class imbalanced problems. We propose a novel two-phase oversampling approach involving knowledge transfer that has the synergy of SMOTE and GAN. The unrealistic or overgeneralized samples of SMOTE are transformed into realistic distribution of data by GAN where there is not enough minority class data available for GAN to process them by itself effectively. We named it SMOTified-GAN as GAN works on pre-sampled minority data produced by SMOTE rather than randomly generating the samples itself. The experimental results prove the sample quality of minority class(es) has been improved in a variety of tested benchmark datasets. Its performance is improved by up to 9% from the next best algorithm tested on F1-score measurements. Its time complexity is also reasonable which is around O(N 2 d 2 T ) for a sequential algorithm.INDEX TERMS Generative Adversarial Network (GAN), Synthetic Minority Over-Sampling Technique (SMOTE), SMOTified-GAN, class imbalance problem.

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Push for More: On Comparison of Data Augmentation and SMOTE with Optimised Deep Learning Architecture for Side-Channel

Cited by 7 publications

References 11 publications

Improving Deep Learning Networks for Profiled Side-channel Analysis Using Performance Improvement Techniques

Improving Deep Learning Networks for Profiled Side-channel Analysis Using Performance Improvement Techniques

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

SMOTified-GAN for Class Imbalanced Pattern Classification Problems

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