Multiple-Input, Multilayer-Perception-Based Classification of Traces From Side-Channel Attacks

Computer ◽  
2020 ◽  
Vol 53 (8) ◽  
pp. 40-48
Author(s):  
Hanwen Feng ◽  
Jing Zhou ◽  
Weiguo Lin ◽  
Yujuan Zhang ◽  
Zhiguo Qu
Keyword(s):  
Side Channel ◽  
Side Channel Attacks ◽  
Multilayer Perception ◽  
Multiple Input

scholarly journals MLP and CNN-based Classification of Points of Interest in Side-channel Attacks

2020 ◽  
Vol 8 (2) ◽  
pp. 108
Author(s):  
Hanwen Feng ◽  
Weiguo Lin ◽  
Wenqian Shang ◽  
Jianxiang Cao ◽  
Wei Huang
Keyword(s):  
Side Channel ◽  
Side Channel Attacks ◽  
Points Of Interest

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.

scholarly journals Systematic Classification of Side-Channel Attacks: A Case Study for Mobile Devices

2018 ◽  
Vol 20 (1) ◽  
pp. 465-488 ◽  
Author(s):  
Raphael Spreitzer ◽  
Veelasha Moonsamy ◽  
Thomas Korak ◽  
Stefan Mangard
Keyword(s):  
Mobile Devices ◽  
Side Channel ◽  
Side Channel Attacks ◽  
Systematic Classification

Countermeasure for Cryptographic Chips to Resist Side-Channel Attacks

2009 ◽  
Vol 19 (11) ◽  
pp. 2990-2998 ◽  
Author(s):  
Tao ZHANG ◽  
Ming-Yu FAN
Keyword(s):  
Side Channel ◽  
Side Channel Attacks

scholarly journals ThermalAttackNet: Are CNNs Making It Easy to Perform Temperature Side-Channel Attack in Mobile Edge Devices?

2021 ◽  
Vol 13 (6) ◽  
pp. 146
Author(s):  
Somdip Dey ◽  
Amit Kumar Singh ◽  
Klaus McDonald-Maier
Keyword(s):  
Information Flow ◽  
Heat Dissipation ◽  
Side Channel ◽  
Side Channel Attack ◽  
Side Channel Attacks ◽  
Information Flow Control ◽  
On Chip ◽  
Temperature Side ◽  
Over Time ◽  
Memory Efficient

Side-channel attacks remain a challenge to information flow control and security in mobile edge devices till this date. One such important security flaw could be exploited through temperature side-channel attacks, where heat dissipation and propagation from the processing cores are observed over time in order to deduce security flaws. In this paper, we study how computer vision-based convolutional neural networks (CNNs) could be used to exploit temperature (thermal) side-channel attack on different Linux governors in mobile edge device utilizing multi-processor system-on-chip (MPSoC). We also designed a power- and memory-efficient CNN model that is capable of performing thermal side-channel attack on the MPSoC and can be used by industry practitioners and academics as a benchmark to design methodologies to secure against such an attack in MPSoC.

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