The implementation of chaos-based PUF designs in field programmable gate array

Tuncer, Taner

doi:10.1007/s11071-016-2938-3

Cited by 38 publications

(14 citation statements)

References 30 publications

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“…[57] proposed a TRNGs with graphics processing units as the source of entropy, unpredictable behavior of which was managed by computing logistic maps, and high throughput achieved 447.83 Mbit/s. Tuncer [58] applied the random challenges from the logistic map to physical unclonable functions based on ring oscillator (RO-PUF) to generate random numbers in real time in FPGA, which prevented the PUF from being attacked (cracked) and improved the randomness of the random numbers. Because the value distribution of logistic mapping points is too centralized and blank bands appear in other regions, the uniform distribution characteristics of the mapping points are poor, so that logistic mapping needs post-processing (Von Neumann corrector [59], XOR correctors [60], one-way hash function [61], etc.)…”

Section: Logistic Mappingmentioning

confidence: 99%

A Survey on True Random Number Generators Based on Chaos

Tang

et al. 2019

Discrete Dynamics in Nature and Society

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With the rapid development of communication technology and the popularization of network, information security has been highly valued by all walks of life. Random numbers are used in many cryptographic protocols, key management, identity authentication, image encryption, and so on. True random numbers (TRNs) have better randomness and unpredictability in encryption and key than pseudorandom numbers (PRNs). Chaos has good features of sensitive dependence on initial conditions, randomness, periodicity, and reproduction. These demands coincide with the rise of TRNs generating approaches in chaos field. This survey paper intends to provide a systematic review of true random number generators (TRNGs) based on chaos. Firstly, the two kinds of popular chaotic systems for generating TRNs based on chaos, including continuous time chaotic system and discrete time chaotic system are introduced. The main approaches and challenges are exposed to help researchers decide which are the ones that best suit their needs and goals. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. We also devote a part of the paper to review TRNGs based on current-mode chaos for this problem. Finally, quantitative results are given for the described methods in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of TRNGs based on chaos.

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Section: Logistic Mappingmentioning

confidence: 99%

A Survey on True Random Number Generators Based on Chaos

Tang

et al. 2019

Discrete Dynamics in Nature and Society

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“…In the literature, ROs, in which jitter represents the source of randomness, are also used in Physically Unclonable Function (PUF) based TRNG designs [20][21]. In [20], for two separate PUF circuits in the system, 64 ROs, each consisting of 13 inverters, were used.…”

Section: Related Workmentioning

confidence: 99%

“…Random numbers obtained from the RO-PUF (Ring Oscillator-Physically Unclonable Function) implemented on two different FPGA cores against the same query were passed through the post-processing technique, and successful results were obtained. In [21], the query input of the PUF circuit with 128 ROs, each consisting of 3 inverters, was obtained from the logistic map with chaotic behavior. In [4] and [12], instead of ROs, jitter signals obtained by the numerical implementation of analog PLL components were used.…”

Section: Related Workmentioning

confidence: 99%

Implementation of a Digital TRNG Using Jitter Based Multiple Entropy Source on FPGA

Gari̇pcan

Erdem

2019

Informacije MIDEM

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In this study, hardware implementation and evaluation of a true random number generator (TRNG) is presented. For the implementation, Field Programmable Gate Array (FPGA) hardware, in which numerical processes based on an algorithmic basis are carried out, was used. In the system, ring oscillators (ROs) with similar structures were used as a noise source, and true randomness was obtained by sampling the jitter signals originating from the oscillators. However, the most critical cryptographic disadvantage of jitterbased TRNGs is the statistical inadequacy of the system. At this point, in contrast to existing designs, entropy sources derived from the subsets of ROs were used in the sampling and post-processing stage. The statistical quality of the system was improved by using true random numbers/inputs obtained from these entropy sources in the sampling and post-processing stage. With sampling and postprocessing inputs, the use of complex post-processing techniques that limit the output bit rate of the generator in the system was not required. Thus, a high-performance adaptable TRNG model with reduced hardware resource consumption is obtained. The statistical validation of the TRNG, which was tested in 6 different scenarios for two separate ring oscillator (RO) architectures and three different operating frequencies, was performed with the NIST 800-22 and AIS31 test packages.

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“…Chaotic systems (CSs) are one of the areas that have been studied extensively in recent years [1][2][3]. CSs can be defined as nonlinear systems that exhibit an infinite number of non-periodic oscillations with a specific order within certain intervals [4,5]. The sensitivity to the initial conditions (ICs) and the system parameters are among the important characteristics of the CSs [6,7].…”

Section: Introductionmentioning

confidence: 99%

CCII current conveyor and dormand-prince-based chaotic oscillator designs for secure communication applications

Alçın

Tuna

Pehlivan

et al. 2020

International Advanced Researches and Engineering Journal

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Chaos is one of the important research areas in recent years. The chaotic signal generator is one of the most basic structure in the chaos-based researches and applications. In this study, Sundarapandian-Pehlivan Chaotic Oscillator (SPCO) designs have been implemented in 2 different platforms as analog-based using Second-Generation Current Controlled Current Conveyor (CCII) and FPGA-based with one of the chaotic oscillator that has been presented to the literature namely Sundarapandian-Pehlivan system. The structure used for the design of CCII-based chaotic oscillator and the results obtained from the study have been presented. In the second phase, the design of SPCO has been realized in order to utilize for running in FPGA chips using Dormand-Prince (DP) numeric algorithm. The design has been coded in VHDL using 32-bit IEEE-754-1985 floating point representation. The designed system has been tested by synthesizing it in Xilinx ISE Design Tools program. Then, the test results obtained from DPbased SPCO structure have been presented. In the last phase, the designed system has been synthesized for VIRTEX-7 FPGA. FPGA chip resource consumption values that obtained after the Place-Route process are presented. According to the results, the maximum operating frequency of DP-based SPCO unit on FPGA is obtained as 362.608 MHz. In future studies, the designs of Pseudo Random Number Generator (RNG) and True RNG can be performed using DP-based SPCO unit implemented successfully in this study.

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The implementation of chaos-based PUF designs in field programmable gate array

Cited by 38 publications

References 30 publications

A Survey on True Random Number Generators Based on Chaos

A Survey on True Random Number Generators Based on Chaos

Implementation of a Digital TRNG Using Jitter Based Multiple Entropy Source on FPGA

CCII current conveyor and dormand-prince-based chaotic oscillator designs for secure communication applications

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