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Related Concept Videos

Random Variables01:09

Random Variables

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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
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The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Wald-Wolfowitz Runs Test II01:17

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The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Related Experiment Videos

Deep Learning-Based Security Verification for a Random Number Generator Using White Chaos.

Cai Li1,2, Jianguo Zhang1,2, Luxiao Sang1,2

  • 1Key Laboratory of Advanced Transducers and Intelligent Control System, Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary
This summary is machine-generated.

A deep learning model analyzed a chaotic random number generator. The model found the generator highly resistant to prediction, demonstrating its security for generating random numbers.

Keywords:
deep learningpredictive modelrandom number generatorsecurity analysissemiconductor laserwhite chaos

Related Experiment Videos

Area of Science:

  • Applied Physics
  • Computer Science
  • Information Security

Background:

  • Assessing the security of random number generators (RNGs) is crucial for cryptography and simulations.
  • Chaos-based RNGs offer potential for high-quality randomness but require rigorous security analysis.
  • Deep learning (DL) presents a powerful tool for analyzing complex temporal patterns.

Purpose of the Study:

  • To propose and evaluate a DL-based predictive analysis for assessing the security of a non-deterministic random number generator (NRNG) utilizing white chaos.
  • To investigate the predictive capability of a temporal pattern attention (TPA)-based DL model on NRNG data.
  • To demonstrate the NRNG's resistance to sophisticated predictive modeling.

Main Methods:

  • Employed a temporal pattern attention (TPA)-based deep learning model to analyze data from two stages of the NRNG.
  • Applied the DL model to output data from a chaotic external-cavity semiconductor laser (ECL) and the final NRNG output.
  • Validated the DL model's predictive power on a linear congruential algorithm (LCA) based RNG.

Main Results:

  • The DL model successfully detected correlations in the initial ECL stage data, attributed to time-delay signatures.
  • After optical heterodyning of two chaotic ECLs and minimal post-processing, the DL model found no discernible patterns in the NRNG output.
  • The NRNG demonstrated strong resistance against the predictive DL model, indicating enhanced security.

Conclusions:

  • The proposed DL-based predictive analysis effectively evaluates the security of chaotic NRNGs.
  • The investigated NRNG exhibits robust resistance to sophisticated predictive attacks.
  • DL models can serve as valuable tools for assessing the security and quality of random number generators.