It may be time to improve the neuron of artificial neural network

Liu, Gang

doi:10.36227/techrxiv.12477266.v11

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…Likewise, in the network dimension, the fusion technique combines the distinct learning preferences of each sub-network, leading to a fusion network with improved performance. Specifically, the Residual Dendrite (ResDD) network can enhance the data processing capability of the DD network through the use of residual modules, effectively mitigating the risk of overfitting [14,15].…”

Section: Introductionmentioning

confidence: 99%

Multi-Dimensional Fusion Deep Learning for Side Channel Analysis

Deng,

Wang,

et al. 2023

Electronics

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The rapid advancement of deep learning has significantly heightened the threats posed by Side-Channel Attacks (SCAs) to information security, transforming their effectiveness to a degree several orders of magnitude superior to conventional signal processing techniques. However, the majority of existing Deep-Learning Side-Channel Attacks (DLSCAs) primarily focus on the classification accuracy of the trained model at the attack stage, often assuming that adversaries have unlimited computational and time resources during the profiling stage. This might result in an inflated assessment of the trained model’s fitting capability in a real attack scenario. In this paper, we present a novel DLSCA model, called a Multi-Dimensional Fusion Convolutional Residual Dendrite (MD_CResDD) network, to enhance and speed up the feature extraction process by incorporating a multi-scale feature fusion mechanism. By testing the proposed model on two software implementations of AES-128, we show that it is feasible to improve the profiling speed by at least 34% compared to other existing deep-learning models for DLSCAs and meanwhile achieved a certain level of improvement (8.4% and 0.8% for two implementations) in the attack accuracy. Furthermore, we also investigate how different fusion approaches, fusion times, and residual blocks can affect the attack efficiency on the same two datasets.

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Section: Introductionmentioning

confidence: 99%

Multi-Dimensional Fusion Deep Learning for Side Channel Analysis

Deng,

Wang,

et al. 2023

Electronics

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“…However, the resulting model is often a black box model that is difficult to understand and explain. The DD [29] is a white-box algorithm that functions as a logic extractor, enabling it to classify input data without the need to find classification curves or surfaces. It accomplishes this by extracting logical relationship information between input data.…”

Section: Introduction Of Ddmentioning

confidence: 99%

Research on Pattern Recognition Method for φ-OTDR System Based on Dendrite Net

Chen,

Yang,

et al. 2023

Electronics

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The phase-sensitive optical time-domain reflectometer (φ-OTDR) is commonly used in various industries such as oil and gas pipelines, power communication networks, safety maintenance, and perimeter security. However, one challenge faced by the φ-OTDR system is low pattern recognition accuracy. To overcome this issue, a Dendrite Net (DD)-based pattern recognition method is proposed to differentiate the vibration signals detected by the φ-OTDR system, and normalize the differential signals with the original signals for feature extraction. These features serve as input for the pattern recognition task. To optimize the DD for the pattern recognition of the feature vectors, the Variable Three-Term Conjugate Gradient (VTTCG) is employed. The experimental results demonstrate the effectiveness of the proposed method. The classification accuracy achieved using this method is 98.6%, which represents a significant improvement compared to other techniques. Specifically, the proposed method outperforms the DD, Support Vector Machine (SVM), and Extreme Learning Machine (ELM) by 7.5%, 8.6%, and 1.5% respectively. The findings of this research paper indicate that the pattern recognition method based on DD and optimized using the VTTCG can greatly enhance the accuracy of the φ-OTDR system. This improvement has important implications for various applications in industries such as pipeline monitoring, power communication networks, safety maintenance, and perimeter security.

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Machine learning based screening of organic frameworks for separation of CF4/N2, C2F6/N2, and SF6/N2

Peng,

Wang

2024

Chemical Engineering Science

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It may be time to improve the neuron of artificial neural network

Cited by 4 publications

References 29 publications

Multi-Dimensional Fusion Deep Learning for Side Channel Analysis

Multi-Dimensional Fusion Deep Learning for Side Channel Analysis

Research on Pattern Recognition Method for φ-OTDR System Based on Dendrite Net

Machine learning based screening of organic frameworks for separation of CF4/N2, C2F6/N2, and SF6/N2

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