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  • Ký hiệu PL/XG: 006.3
    Nhan đề: Efficient CNN - Based Profiled side Channel Attacks /
DDC 006.3
Tác giả CN Tran, Ngoc Quy
Tác giả TT
Nhan đề Efficient CNN - Based Profiled side Channel Attacks / Tran Ngoc Quy
Tóm tắt 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.
Từ khóa tự do Convolutional neural network
Từ khóa tự do Grey wolf opimise
Từ khóa tự do Profiled attack
Từ khóa tự do Side channel attack
Tác giả(bs) CN Nguyen, Hong Quang
Nguồn trích Tạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics 2021tr. 3-24 Số: 01
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040 |aACTVN
041 |avie
044 |avm
082 |a006.3
10010|aTran, Ngoc Quy
110 |bVietnam Academy Of Science And Technology
245 |aEfficient CNN - Based Profiled side Channel Attacks / |cTran Ngoc Quy
520 |aProfiled 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.
653 |aConvolutional neural network
653 |aGrey wolf opimise
653 |aProfiled attack
653 |aSide channel attack
700 |aNguyen, Hong Quang
7730 |tTạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics |d2021|gtr. 3-24|x1813-9663|i01
890|a0|b0|c0|d0
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