A Chaotic Ring oscillator based Random Number Generator

Dhanuskodi, Siva Nishok; Vijayakumar, Arunkumar; Kundu, Sandip

doi:10.1109/hst.2014.6855588

Cited by 27 publications

(8 citation statements)

References 20 publications

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“…The most well known is probably Chua's circuit, 10 shown by Galias 11 to be chaotic, and studied quite extensively since (e.g., by Kuznetsov et al 12,13 ). Closer to the current idea are the ring oscillator-based circuits proposed by Hosokawa and Nishio 14 and by Dhanuskodi et al 15 These designs, however, are less portable than the one proposed here, because they are not all-digital. The circuit of the current paper will be analyzed by methods derived from the theory of Glass networks, as used in qualitative modeling of gene regulation.…”

Section: Introductionmentioning

confidence: 87%

Chaos in a ring circuit

Farcot

Best

Edwards

et al. 2019

Chaos: An Interdisciplinary Journal of Nonlinear Science

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A ring-shaped logic circuit is proposed here as a robust design for a True Random Number Generator (TRNG). Most existing TRNGs rely on physical noise as a source of randomness, where the underlying idealized deterministic system is simply oscillatory. The design proposed here is based on chaotic dynamics and therefore intrinsically displays random behavior, even in the ideal noise-free situation. The paper presents several mathematical models for the circuit having di erent levels of detail. They take the form of di erential equations using steep sigmoid terms for the transfer functions of logic gates. A large part of the analysis is concerned with the hard step-function limit, leading to a model known in mathematical biology as a Glass network. In this framework, an underlying discrete structure (a state space diagram) is used to describe the likely structure of the global attractor for this system. The latter takes the form of intertwined periodic paths, along which trajectories alternate unpredictably. It is also invariant under the action of the cyclic group. A combination of analytical results and numerical investigations con rms the occurrence of symmetric chaos in this system, which when implemented in (noisy) hardware, should therefore serve as a robust TRNG.

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Section: Introductionmentioning

confidence: 87%

Chaos in a ring circuit

Farcot

Best

Edwards

et al. 2019

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…On the other hand, TRNG circuits play a critical role in hardware security. TRNGs based on dual oscillator architecture and a chaotic ring oscillator have been proposed in [17][18], respectively.…”

Section: Literature Survey-chaos Theorymentioning

confidence: 99%

A Mathematical Approach Towards Random Road Profile Generation Based on Chaotic Signals of Chua's Circuit

Milani

Momtaz

Amini

et al. 2022

Contemporary Mathematics

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In response to application demands in vehicle dynamics and control, traffic engineering, urban planning, and logistics, the generation of an adequate artificial road profile in terms of the diversity of geometric scenarios has been addressed in the current manuscript. The underlying mathematical principles for generating a geometrically comprehensive, yet logically meaningful, 3D road profile have been taken from high and unbiased sweeping factors of random number sequences over their domain of interest. And to generate such random number sequences, the mathematically manipulated output signal of a well-established chaotic system has been utilized, namely that of the Chua's circuit. Having defined the target road profile mathematically with all its geometrical complexities, a suitable scheme derived from the mentioned chaotic signal has been used to generate the required random number sequences as defining parameters of the road profile. The scheme has been otherwise tested and proven to show the demanded level of randomness in literature. Several attempts have been made to create a diverse range of road profiles, considering the constraints imposed by vehicle dynamics. To generate the road geometries, the limitations imposed by the vehicle's motion, such as the limitations on corresponding curvatures, slopes, and banking angles are negotiated, in terms of vehicle dynamics and available tire-road friction forces, by evaluating how close a vehicle will be to its tire force capacity limits as it travels on sections of the generated road.

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“…It can be seen that this chaotic oscillator consisted of three parts of circuit, a nonlinear circuit to represents the chaotic map, two sample-and-hold circuits (S/H) to track and store the signals as a memory and the bu er to carry signals to the next stage implemented by a two-stage operational ampli er (op-amp). Dhanuskodi et al [69] had proposed a TRNG based on chaotic ring oscillator. In order to pass the statistical test, XOR was used to postprocess the random number generated by the chaotic ring oscillator.…”

Section: Discrete Time Chaotic Oscillator Discrete Time Chaoticmentioning

confidence: 99%

“…In [69], besides the inherent chaos, the oscillator also collects physical noise, and uses the combination of two light sources to improve the output speed of the oscillator. Compared with the TRNG's area of the same CMOS process, under 0.35 µm CMOS process in [48], [62] and [69], the largest area is [69]. e reason is that the authors used eight-stage pipeline ADC and two-stage pipeline ADC to design circuits.…”

Section: Piece-wisementioning

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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A Chaotic Ring oscillator based Random Number Generator

Cited by 27 publications

References 20 publications

Chaos in a ring circuit

Chaos in a ring circuit

A Mathematical Approach Towards Random Road Profile Generation Based on Chaotic Signals of Chua's Circuit

A Survey on True Random Number Generators Based on Chaos

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