A novel secure chaos-based pseudo random number generator based on ANN-based chaotic and ring oscillator: design and its FPGA implementation

被引:28
作者
Tuna, Murat [1 ]
机构
[1] Kirklareli Univ, Tech Sci Vocat Sch, Dept Elect, Kirklareli, Turkey
关键词
Artificial neural networks; Tansig activation function; PRNG; Chaotic systems; Ring oscillator; FPGA; NIST; NEURAL-NETWORK IMPLEMENTATION; REAL-TIME; HARDWARE IMPLEMENTATION; SYSTEM; REALIZATION; PRNG; TRNG; MAP;
D O I
10.1007/s10470-020-01703-z
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:167 / 181
页数:15
相关论文
共 79 条
[41]  
Mohd-Yasin F, 2004, 16TH INTERNATIONAL CONFERENCE ON MICROELECTRONICS, PROCEEDINGS, P458
[42]   A novel pseudorandom number generator based on pseudorandomly enhanced logistic map [J].
Murillo-Escobar, M. A. ;
Cruz-Hernandez, C. ;
Cardoza-Avendano, L. ;
Mendez-Ramirez, R. .
NONLINEAR DYNAMICS, 2017, 87 (01) :407-425
[43]  
Nilsson P, 2014, 2014 NORCHIP
[44]   FPGA Implementation of the Multilayer Neural Network for the Speed Estimation of the Two-Mass Drive System [J].
Orlowska-Kowalska, Teresa ;
Kaminski, Marcin .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2011, 7 (03) :436-445
[45]   Fully parallel ANN-based arrhythmia classifier on a single-chip FPGA: FPAAC [J].
Ozdemir, Ahmet Turan ;
Danisman, Kenan .
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, 2011, 19 (04) :667-687
[46]   Cryptographically secure random number generator with chaotic additional input [J].
Ozkaynak, Fatih .
NONLINEAR DYNAMICS, 2014, 78 (03) :2015-2020
[47]   PRNG Based on Skew Tent Map [J].
Palacios-Luengas, L. ;
Pichardo-Mendez, J. L. ;
Diaz-Mendez, J. A. ;
Rodriguez-Santos, F. ;
Vazquez-Medina, R. .
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2019, 44 (04) :3817-3830
[48]  
Patidar V., 2009, Electronic Journal of Theoretical Physics, V20, P327
[49]   Megastability in a quasi-periodically forced system exhibiting multistability, quasi-periodic behaviour, and its analogue circuit simulation [J].
Prakash, Pankaj ;
Rajagopal, K. ;
Singh, J. P. ;
Roy, B. K. .
AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS, 2018, 92 :111-115
[50]   Dynamical analysis, sliding mode synchronization of a fractional-order memristor Hopfield neural network with parameter uncertainties and its non-fractional-order FPGA implementation [J].
Rajagopal, Karthikeyan ;
Tuna, Murat ;
Karthikeyan, Anitha ;
Koyuncu, Ismail ;
Duraisamy, Prakash ;
Akgul, Akif .
EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS, 2019, 228 (10) :2065-2080