Chaos-Based Bitwise Dynamical Pseudorandom Number Generator On FPGA

In this paper, a new pseudorandom number generator (PRNG) based on the logistic map has been proposed. To prevent the system to fall into short period orbits as well as increasing the randomness of the generated sequences, the proposed algorithm dynamically changes the parameters of the chaotic system. This PRNG has been implemented in a Virtex 7 field-programmable gate array (FPGA) with a 32-bit fixed point precision, using a total of 510 lookup tables (LUTs) and 120 registers. The sequences generated by the proposed algorithm have been subjected to the National Institute of Standards and Technology (NIST) randomness tests, passing all of them. By comparing the randomness with the sequences generated by a raw 32-bit logistic map, it is shown that, by using only an additional 16% of LUTs, the proposed PRNG obtains a much better performance in terms of randomness, increasing the NIST passing rate from 0.252 to 0.989. Finally, the proposed bitwise dynamical PRNG is compared with other chaos-based realizations previously proposed, showing great improvement in terms of resources and randomness.

[1]  Mounir Boukadoum,et al.  Pseudorandom Stimuli Generation for Testing Time-to-Digital Converters on an FPGA , 2009, IEEE Transactions on Instrumentation and Measurement.

[2]  A. Dandache,et al.  Real-time FPGA implementation of Lorenz's chaotic generator for ciphering telecommunications , 2009, 2009 Joint IEEE North-East Workshop on Circuits and Systems and TAISA Conference.

[3]  George A. Kaminski,et al.  Quality of random number generators significantly affects results of Monte Carlo simulations for organic and biological systems , 2011, J. Comput. Chem..

[4]  B. Harris PROBABILITY DISTRIBUTIONS RELATED TO RANDOM MAPPINGS , 1960 .

[5]  Santiago Celma,et al.  A new simple technique for improving the random properties of chaos-based cryptosystems , 2018 .

[6]  Jeaneth Machicao,et al.  Improving the pseudo-randomness properties of chaotic maps using deep-zoom. , 2016, Chaos.

[7]  Rafael Bidarra,et al.  Procedural Generation of Dungeons , 2014, IEEE Transactions on Computational Intelligence and AI in Games.

[8]  Cuauhtemoc Mancillas-López,et al.  Hardware implementation of pseudo-random number generators based on chaotic maps , 2017 .

[9]  Elaine B. Barker,et al.  A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications , 2000 .

[10]  Xiaomin Li,et al.  Dual-Channel Pseudorandom Sequence Generator With Precise Time Delay Between Its Two Channels , 2008, IEEE Transactions on Instrumentation and Measurement.

[11]  Mohammad Hossein Madani,et al.  New methods for enhancing fine acquisition in dual folding algorithm of long pseudo noise codes , 2018, Int. J. Commun. Syst..

[12]  R. Povinelli,et al.  Analyzing Logistic Map Pseudorandom Number Generators for Periodicity Induced by Finite Precision Floating-Point Representation , 2012 .

[13]  T. Addabbo,et al.  The Digital Tent Map: Performance Analysis and Optimized Design as a Low-Complexity Source of Pseudorandom Bits , 2006, IEEE Transactions on Instrumentation and Measurement.

[14]  Carlos Sánchez-Azqueta,et al.  Application of a MEMS-Based TRNG in a Chaotic Stream Cipher , 2017, Sensors.

[15]  Concepción Aldea,et al.  A New Technique For Improving the Security of Chaos Based Cryptosystems , 2018, 2018 IEEE International Symposium on Circuits and Systems (ISCAS).

[16]  Joseph Zambreno,et al.  A chaotic encryption scheme for real-time embedded systems: design and implementation , 2013, Telecommun. Syst..

[17]  José M. Amigó,et al.  Chaos-Based Cryptography , 2009, Intelligent Computing Based on Chaos.

[18]  P. Dabal,et al.  FPGA implementation of chaotic pseudo-random bit generators , 2012, Proceedings of the 19th International Conference Mixed Design of Integrated Circuits and Systems - MIXDES 2012.