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Efficient CNN - Based Profiled side Channel Attacks / Tran Ngoc Quy // Tạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics . - 2021. - tr. 3-24. - ISSN: 1813-9663
Ký hiệu phân loại (DDC): 006.3
Profiled side—channel attacks are now considered as a pawn-7:33} form of side channel attacks used to break the security of cryptographic devices. A recent line of Menard: has investigated a new profiled attack based on deep learning and many of them have used com/olivine neural network (CNN) as deep learning architecture for the attack. The effectiveness of the attack in greatiy irilzenced by the CNN architecture. However. the CNN architecture used for current profiled amen-2;: have often been based on image recognition fields. and choosing the right CNN architectures and pa:a:;eters for adaption to profiled attacks is still challenging. In this paper, we propose an efficient pureed attack for unprotected and masking-protected cryptographic devices based on two CNN arr-2.1: aft-g: es. Called CNNn, CNNd respectively. Both of CNN architecture parameters proposed in this pap-3: are based on the property of points of interest on the power trace and further determined 0:: {Le Gny \Yolf Optimization (GVVO) algorithm. To verify the proposed attacks, experiments were Cr".'7_1;z’~.i on a trace set collected from an Atmega8515 smart card when it performs AES-l‘lS en-grjgtaca. a DPA contest v4 dataset and the ASCAD public dataset.
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Efficient Cnn—Based Profiled Side Channel Attacks / Tran Ngọc Quy, Nguyen Hong Quang // Tạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics . - 2021. - tr. 3-24. - ISSN: 1813-9663
Ký hiệu phân loại (DDC): 006.3
Profiled side—channel attacks are now considered as a pawn-7:33} form of side channel attacks used to break the security of cryptographic devices. A recent line of Menard: has investigated a new profiled attack based on deep learning and many of them have used com/olivine neural network (CNN) as deep learning architecture for the attack. The effectiveness of the attack in greatiy irilzenced by the CNN architecture. However. the CNN architecture used for current profiled amen-2;: have often been based on image recognition fields. and choosing the right CNN architectures and pa:a:;eters for adaption to profiled attacks is still challenging. In this paper, we propose an efficient pureed attack for unprotected and masking-protected cryptographic devices based on two CNN arr-2.1: aft-g: es. Called CNNn, CNNd respectively. Both of CNN architecture parameters proposed in this pap-3: are based on the property of points of interest on the power trace and further determined 0:: {Le Gny \Yolf Optimization (GVVO) algorithm. To verify the proposed attacks, experiments were Cr".'7_1;z’~.i on a trace set collected from an Atmega8515 smart card when it performs AES-l‘lS en-grjgtaca. a DPA contest v4 dataset and the ASCAD public dataset.
Số bản sách:
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Tài liệu số:
(1)
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