Introduction to Secure PRNGs

The generation of high quality random numbers is crucial to many cryptographic applications, including cryptographic protocols, secret of keys, nonces or salts. Their values must contain enough randomness to be unpredictable to attackers. Pseudo-random number generators require initial data with high entropy as a seed to produce a large stream of high quality random data. Yet, despite the importance of randomness, proper high quality random number generation is often ignored. Primarily embedded devices often suffer from weak random number generators.In this work, we focus on identifying and evaluating SRAM in commercial off-the-shelf microcontrollers as an entropy source for PRNG seeding. We measure and evaluate the SRAM start-up patterns of two popular types of microcontrollers, a STMicroelectronics STM32F100R8 and a Microchip PIC16F1825. We also present an efficient softwareonly architecture for secure PRNG seeding. After analyzing over 1 000 000 measurements in total, we conclude that of these two devices, the PIC16F1825 cannot be used to securely seed a PRNG. The STM32F100R8, however, has the ability to generate very strong seeds from the noise in its SRAM start-up pattern. These seeds can then be used to ensure a PRNG generates high quality data.

show abstract

“…An introduction to PRNGs can be found in [1]. The output of such a generator only seems random to observers without prior knowledge.…”

Section: Random Number Generationmentioning

confidence: 99%

Secure PRNG seeding on commercial off-the-shelf microcontrollers

Herrewege

Leest²,

Schaller

et al. 2013

Proceedings of the 3rd International Workshop on Trustworthy Embedded Devices

View full text Add to dashboard Cite

show abstract

“…Furthermore, the Trends Applied Sci. Res., 10 (5): 231-244, 2015 (Walker, 2008;Babaei and Farhadi, 2011;Ilyas et al, 2013), so this pattern completes their evaluation. Because of inappropriate generators that success in all 3 levels, as mentioned in recourse (Wang and Nicol, 2015), implementing iterated logarithm test is an important step for reducing second type error probability that our proposed pattern has focused on it.…”

Section: Eighth Stepmentioning

confidence: 99%

“…2 shows, this pattern neither puts the attack criteria like many recourses (Kelsey et al, 1998(Kelsey et al, , 1999, nor pays no attention to attack like many other recourses (Reyad and Kotulski, 2015;Babaei and Farhadi, 2011;Ilyas et al, 2013;Hamdi et al, 2015). Also, about statistical tests intended by all the researchers, our proposed model completes recourses perspectives only deal with tests Ilyas et al, 2013;Hamdi et al, 2015).…”

Section: Eighth Stepmentioning

confidence: 99%

“…Applying programs such as DIEHARD in reference (Marsaglia, 1996), ENT in reference (Walker, 2008), U0I in reference (L'Ecuyer and Simard, 2007) and NIST in reference (Rukhin et al, 2010) are available. Babaei and Farhadi (2011) suggested that the essentials for a pseudo random number generator are uniform distribution, independence, the long period and unpredictability and offered the use of U0I package for examining these essentials (Babaei and Farhadi, 2011). Yang and Xiao-Jun (2012) applied autocorrelation and NIST tests for the suggestive generators based on empirical distribution of data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A General Evaluation Pattern for Pseudo Random Number Generators

Gaeini¹,

Mirghadri²,

Jandaghi³

2015

Trends in Applied Sciences Research

View full text Add to dashboard Cite

Many models have been designed for generating pseudo random numbers and there is the potentiality to design more. Among the mentioned generators, a few of them are applied in cryptography. Thus, there is an inevitable need among researchers and users for evaluating the generators. A variety of criteria are used for evaluation which have more or less or neutral importance depending on the way the generators are applied. Accordingly, some reliable sources pay attention to the particular criterion while overlooking others. In the present study, in addition to enumerating all of the popular criteria, there has been an attempt to classify them in the form of a general model. At the end, some suggestions are presented for the foremost priority in using and applying the criteria. With implementing this pattern all the criterions have been considered and on the other hand the probability of second type error will be reduced. Also, observance of the sequence of criterions would cause saving in time and costs.

show abstract

“…Pseudorandom number generators (PRNGs) [6][7][8] are a vital area of research due to its ubiquitous use [9] in a variety of applications such as cryptography. In particular, the chaotic map has been designed for many PRNGs.…”

Section: Introductionmentioning

confidence: 99%

Performance of Finite Precision on Discrete Chaotic Map Based on a Feedback Shift Register

Huang

Ding

2020

Complexity

View full text Add to dashboard Cite

The scintillating technological improvements have changed the process of communication in all parts of the world. Shopping, banking, instant communication, and so on can be operated online and do not care about the linkage of those Internet communications. Because of simple chaotic structure, discrete nature, less arithmetic computation, and high complexity, low-dimensional chaotic systems such as a logistic map and tent map are more attractive than a high-dimensional chaotic system. To overcome the disadvantages of a low-dimensional chaotic map with a finite precision in chaos-based application, the chaotic map with a feedback shift register (CMFSR) is proposed. The related properties and performance associated with CMFSR are analysed. The relationship among the precision of the system, the architecture, and the performance are discussed. The experiments about new pseudorandom number generator (PRNG) based on CMFSR show that our scheme is simple, secure, and easy to accomplish. Experiments show that the proposed architecture of CMFSR is secure for random number generation.

show abstract

Introduction to Secure PRNGs

Cited by 10 publications

References 15 publications

Secure PRNG seeding on commercial off-the-shelf microcontrollers

Secure PRNG seeding on commercial off-the-shelf microcontrollers

A General Evaluation Pattern for Pseudo Random Number Generators

Performance of Finite Precision on Discrete Chaotic Map Based on a Feedback Shift Register

Contact Info

Product

Resources

About