A Strict Key Enumeration Algorithm for Dependent Score Lists of Side-Channel Attacks

Author(s):  
Yang Li ◽  
Shuang Wang ◽  
Zhibin Wang ◽  
Jian Wang
Author(s):  
Nicolas Veyrat-Charvillon ◽  
Benoît Gérard ◽  
Mathieu Renauld ◽  
François-Xavier Standaert

Author(s):  
Andreas Wiemers ◽  
Johannes Mittmann

AbstractRecent publications consider side-channel attacks against the key schedule of the Data Encryption Standard (DES). These publications identify a leakage model depending on the XOR of register values in the DES key schedule. Building on this leakage model, we first revisit a discrete model which assumes that the Hamming distances between subsequent round keys leak without error. We analyze this model formally and provide theoretical explanations for observations made in previous works. Next we examine a continuous model which considers more points of interest and also takes noise into account. The model gives rise to an evaluation function for key candidates and an associated notion of key ranking. We develop an algorithm for enumerating key candidates up to a desired rank which is based on the Fincke–Pohst lattice point enumeration algorithm. We derive information-theoretic bounds and estimates for the remaining entropy and compare them with our experimental results. We apply our attack to side-channel measurements of a security controller. Using our enumeration algorithm we are able to significantly improve the results reported previously for the same measurement data.


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

2021 ◽  
Vol 13 (6) ◽  
pp. 146
Author(s):  
Somdip Dey ◽  
Amit Kumar Singh ◽  
Klaus McDonald-Maier

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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