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

Sadhana ◽  
2013 ◽  
Vol 38 (3) ◽  
pp. 377-395 ◽  
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

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

2018 ◽  
Vol 28 (01) ◽  
pp. 1950003 ◽  
Author(s):  
E. Saeedi ◽  
M. S. Hossain ◽  
Y. Kong
Keyword(s):  
Neural Networks ◽  
Back Propagation ◽  
Side Channel ◽  
Side Channel Attacks ◽  
Feed Forward ◽  
Feed Forward Back Propagation ◽  
Channel Information ◽  
Components Analysis ◽  
Multi Class Classification

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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