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A novel secure chaos-based pseudo random number generator based on ANN-based chaotic and ring oscillator: design and its FPGA implementation
- Source :
- Analog Integrated Circuits and Signal Processing. 105:167-181
- Publication Year :
- 2020
- Publisher :
- Springer Science and Business Media LLC, 2020.
-
Abstract
- This paper presents a novel, real time, high speed and robust chaos-based pseudo random number generator (PRNG) design using the structures of artificial neural network (ANN)-based 2D chaotic oscillator and ring oscillator. In this study, four different robust PRNGs have been implemented using four different approaches (TS-55, Elliott-93, Elliott-2, Cordic-LUT) of TanSig activation functions (TSAF) that have been used in the design of ANN-based 2D chaotic oscillators. The designs have been coded in VHDL using IEEE-754-1985 number standard. The PRNGs have been synthesized for Virtex-6 FPGA chip using Xilinx ISE Design Tools. After Place&Route operation, FPGA chip statistics and maximum operating frequencies have been presented. The maximum operating frequencies of the proposed PRNGs range between 184 and 241 MHz. The 1 Mbit of bit streams generated by PRNGs have been subjected to NIST-800-22 randomness tests. Among 4 different proposed PRNGs, the proposed PRNGs that designed using the Elliott-93 and Cordic-LUT approaches have successfully passed all NIST-800-22 tests and have a bit production rate of 241 Mbps. The proposed secure hybrid chaos-based PRNG structures were compared with similar studies conducted in the literature in recent years. According to the results, the proposed FPGA-based secure new chaotic PRNG structures are useful in cryptographic applications.
- Subjects :
- Computer science
Chaotic
Cryptography
02 engineering and technology
Ring oscillator
Tansig activation function
NIST
VHDL
0202 electrical engineering, electronic engineering, information engineering
Randomness tests
Hardware_ARITHMETICANDLOGICSTRUCTURES
Field-programmable gate array
FPGA
computer.programming_language
Pseudorandom number generator
Artificial neural networks
Artificial neural network
business.industry
020208 electrical & electronic engineering
Chaotic systems
020206 networking & telecommunications
Surfaces, Coatings and Films
PRNG
Computer engineering
Hardware and Architecture
Signal Processing
business
computer
Subjects
Details
- ISSN :
- 15731979 and 09251030
- Volume :
- 105
- Database :
- OpenAIRE
- Journal :
- Analog Integrated Circuits and Signal Processing
- Accession number :
- edsair.doi.dedup.....46c20f5b88a37a59f527631f6044fca2
- Full Text :
- https://doi.org/10.1007/s10470-020-01703-z