Side Channel Attacks Cryptanalysis against Block Ciphers Based on FPGA Devices

Author(s):  
Anestis Bechtsoudis ◽  
Nicolas Sklavos
2019 ◽  
Vol 9 (7) ◽  
pp. 1438
Author(s):  
HanBit Kim ◽  
Seokhie Hong ◽  
HeeSeok Kim

A masking method is a widely known countermeasure against side-channel attacks. To apply a masking method to cryptosystems consisting of Boolean and arithmetic operations, such as ARX (Addition, Rotation, XOR) block ciphers, a masking conversion algorithm should be used. Masking conversion algorithms can be classified into two categories: “Boolean to Arithmetic (B2A)” and “Arithmetic to Boolean (A2B)”. The A2B algorithm generally requires more execution time than the B2A algorithm. Using pre-computation tables, the A2B algorithm substantially reduces its execution time, although it requires additional space in RAM. In CHES2012, B. Debraize proposed a conversion algorithm that somewhat reduced the memory cost of using pre-computation tables. However, they still require ( 2 ( k + 1 ) ) entries of length ( k + 1 ) -bit where k denotes the size of the processed data. In this paper, we propose a low-memory algorithm to convert A2B masking that requires only ( 2 k ) ( k ) -bit. Our contributions are three-fold. First, we specifically show how to reduce the pre-computation table from ( k + 1 ) -bit to ( k ) -bit, as a result, the memory use for the pre-computation table is reduced from ( 2 ( k + 1 ) ) ( k + 1 ) -bit to ( 2 k ) ( k ) -bit. Second, we optimize the execution times of the pre-computation phase and the conversion phase, and determine that our pre-computation algorithm requires approximately half of the operations than Debraize’s algorithm. The results of the 8/16/32-bit simulation show improved speed in the pre-computation phase and the conversion phase as compared to Debraize’s results. Finally, we verify the security of the algorithm against side-channel attacks as well as the soundness of the proposed algorithm.


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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