Physical Layer Dynamic Key Encryption in OFDM-PON System Based on Cellular Neural Network

Zhou, Yuxin; Bi, Meihua; Zhuo, Xianhao; Lv, Yunxin; Yang, Xuelin; Hu, Weisheng

doi:10.1109/jphot.2021.3059369

Cited by 22 publications

(3 citation statements)

References 27 publications

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“…Designing such behavior using CNN with higher dimensions attracted considerable interest from many studies compared with the low dimensions. As in [40], a 6D-CNN was used to create chaotic sequences to improve the security of the optical communication scheme.…”

Section: Mathematical Modelmentioning

confidence: 99%

FPGA implementation of orthogonal hyperchaotic sequences generator based on the CNN: application in multi-user chaotic communications

Fennouh,

Sadoudi,

Teguig

et al. 2023

Phys. Scr.

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In this article, we propose an efficient orthogonal hyperchaotic sequences generator for application in multi-user chaotic communication systems. The proposed generator consists of two blocks. The first block generates binary hyperchaotic sequences of varying lengths using a 6-Dimensional Cellular Neural Network (CNN) system, offering a wide range of sequence length choices. The second block uses an optimized selection method to generate multiple sets of orthogonal sequences with interesting correlation properties. The method is based on the balance and correlation properties, where zero cross-correlation between any two sequences is considered. A FPGA-based implementation of the proposed generator is presented. The hardware architecture is designed in VHDL and deployed on a Xilinx Virtex-6 FPGA ML605 evaluation kit. The generator is then analyzed and compared with the existing generator in terms of logic area consumption, throughput, latency, and randomness quality. The comparative analysis results show the effectiveness of the proposed generator, which can achieve a high throughput, low latency, and successfully pass all NIST statistical tests. Moreover, the generated orthogonal hyperchaotic sequences exhibit promising performance in a multi-user Differential Chaos Shift Keying (MU-DCSK) system, outperforming the Walsh-Hadamard sequences in terms of bit-error rate under various channel conditions.

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Section: Mathematical Modelmentioning

confidence: 99%

FPGA implementation of orthogonal hyperchaotic sequences generator based on the CNN: application in multi-user chaotic communications

Fennouh,

Sadoudi,

Teguig

et al. 2023

Phys. Scr.

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“…Other applications of Neural Cryptography has allowed for homomorphic encrpytion to be applied to cloud services for secure communication and noise compression, as well as intelligent transportation systems to allow confidentiality of personal information [122,123]. Finally, chaotic NNs has seem many applications as well [124][125][126]. To note, hyper chaotic systems and chaotic Feistel transform and time synchronization with multiple dimensions have allowed the resistance of plain text attacks and brute force attacks within the physical layer [125,126].…”

Section: Applications In Industrymentioning

confidence: 99%

“…Finally, chaotic NNs has seem many applications as well [124][125][126]. To note, hyper chaotic systems and chaotic Feistel transform and time synchronization with multiple dimensions have allowed the resistance of plain text attacks and brute force attacks within the physical layer [125,126].…”

Section: Applications In Industrymentioning

confidence: 99%

The Dichotomy of Neural Networks and Cryptography: War and Peace

Zolfaghari

Koshiba

2022

ASI

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In recent years, neural networks and cryptographic schemes have come together in war and peace; a cross-impact that forms a dichotomy deserving a comprehensive review study. Neural networks can be used against cryptosystems; they can play roles in cryptanalysis and attacks against encryption algorithms and encrypted data. This side of the dichotomy can be interpreted as a war declared by neural networks. On the other hand, neural networks and cryptographic algorithms can mutually support each other. Neural networks can help improve the performance and the security of cryptosystems, and encryption techniques can support the confidentiality of neural networks. The latter side of the dichotomy can be referred to as the peace. There are, to the best of our knowledge, no current surveys that take a comprehensive look at the many ways neural networks are currently interacting with cryptography. This survey aims to fill that niche by providing an overview on the state of the cross-impact between neural networks and cryptography systems. To this end, this paper will highlight the current areas where progress is being made as well as the aspects where there is room for future research to be conducted.

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