Feed-Forward Back-Propagation Neural Networks in Side-Channel Information Characterization

The safety of cryptosystems, mainly based on algorithmic improvement, is still vulnerable to side-channel attacks (SCA) based on machine learning. Multi-class classification based on neural networks and principal components analysis (PCA) can be powerful tools for pattern recognition and classification of side-channel information. In this paper, an experimental investigation was conducted to explore the efficiency of various architectures of feed-forward back-propagation (FFBP) neural networks and PCA against side-channel attacks. The experiment is performed on the data leakage of an FPGA implementation of elliptic curve cryptography (ECC). Our results show that the proposed method is a promising method for SCA with an overall accuracy of 88% correct classification.

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Effective enhancement of classification of respiratory states using feed forward back propagation neural networks

Sadhana ◽

10.1007/s12046-013-0140-6 ◽

2013 ◽

Vol 38 (3) ◽

pp. 377-395 ◽

Cited By ~ 3

Author(s):

A BHAVANI SANKAR ◽

J ARPUTHA VIJAYA SELVI ◽

D KUMAR ◽

K SEETHA LAKSHMI

Keyword(s):

Neural Networks ◽

Back Propagation ◽

Feed Forward ◽

Feed Forward Back Propagation ◽

Back Propagation Neural Networks

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Ensemble Learning Model for Petroleum Reservoir Characterization: A Case of Feed-Forward Back-Propagation Neural Networks

Lecture Notes in Computer Science - Trends and Applications in Knowledge Discovery and Data Mining ◽

10.1007/978-3-642-40319-4_7 ◽

2013 ◽

pp. 71-82 ◽

Cited By ~ 3

Author(s):

Fatai Anifowose ◽

Jane Labadin ◽

Abdulazeez Abdulraheem

Keyword(s):

Neural Networks ◽

Ensemble Learning ◽

Reservoir Characterization ◽

Back Propagation ◽

Learning Model ◽

Petroleum Reservoir ◽

Feed Forward ◽

Feed Forward Back Propagation ◽

Back Propagation Neural Networks

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Fault Identification and Classification of Spur Gearbox with Feed Forward Back Propagation Artificial Neural Network

2012 VI Andean Region International Conference ◽

10.1109/andescon.2012.63 ◽

2012 ◽

Cited By ~ 5

Author(s):

R. Sanchez ◽

A. Arpi ◽

L. Minchala

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Back Propagation ◽

Fault Identification ◽

Feed Forward ◽

Feed Forward Back Propagation ◽

Artificial Neural ◽

Spur Gearbox

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Modeling the Flux Decline during Protein Microfiltration: A Comparison between Feed-Forward Back Propagation and Radial Basis Function Neural Networks

Separation Science and Technology ◽

10.1080/01496395.2012.736914 ◽

2013 ◽

Vol 48 (9) ◽

pp. 1324-1330 ◽

Cited By ~ 4

Author(s):

Sara Ghandehari ◽

Mohammad Mehdi Montazer-Rahmati ◽

Morteza Asghari

Keyword(s):

Neural Networks ◽

Radial Basis Function ◽

Basis Function ◽

Back Propagation ◽

Feed Forward ◽

Feed Forward Back Propagation ◽

Flux Decline ◽

Radial Basis

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River flow forecasting using different artificial neural network algorithms and wavelet transform

Canadian Journal of Civil Engineering ◽

10.1139/l08-090 ◽

2009 ◽

Vol 36 (1) ◽

pp. 26-38 ◽

Cited By ~ 41

Author(s):

Turgay Partal

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Wavelet Transform ◽

Back Propagation ◽

Propagation Model ◽

River Flows ◽

Feed Forward ◽

Feed Forward Back Propagation ◽

Artificial Neural

In this study, the wavelet–neural network structure that combines wavelet transform and artificial neural networks has been employed to forecast the river flows of Turkey. Discrete wavelet transforms, which are useful to obtain to the periodic components of the measured data, have significantly positive effects on artificial neural network modeling performance. Generally, the feed-forward back-propagation method was studied with respect to artificial neural network applications to water resources data. In this study, the performance of generalized neural networks and radial basis neural networks were compared with feed-forward back-propagation methods. Six different models were studied for forecasting of monthly river flows. It was seen that the wavelet and feed-forward back-propagation model was superior to the other models in terms of selected performance criteria.

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