Radio noise use in True Random Number Generation is a popular and well understood strategy, but relatively undermined in research. Given that Very Low Frequency radio streams record chaotic weather events from around the world (like thunderstorms), this novel entropy source sparked our interest. Our choice is supported by prior research in statistical modeling, along with an appropriate combination of both signal and statistical methods. Hence, we describe in detail our processing pipeline for engineering a TRNG from VLF radio noise. It yields high entropy, unbiased and identically distributed outputs that pass FIPS 140-2 tests, regardless of the chosen VLF stream source. To strengthen reproducibility, we have based all of our experiments on publicly available data. We also provide an open-source, modular command-line tool for building stream-based processing pipelines. Furthermore, we offer an example implementing a TRNG from FM radio noise found between standard transmission bands.
Radio noise use in True Random Number Generation is a popular and well understood strategy, but relatively undermined in research. Given that Very Low Frequency radio streams record chaotic weather events from around the world (like thunderstorms), this novel entropy source sparked our interest. Our choice is supported by prior research in statistical modeling, along with an appropriate combination of both signal and statistical methods. Hence, we describe in detail our processing pipeline for engineering a TRNG from VLF radio noise. It yields high entropy, unbiased and identically distributed outputs that pass FIPS 140-2 tests, regardless of the chosen VLF stream source. To strengthen reproducibility, we have based all of our experiments on publicly available data. We also provide an open-source, modular command-line tool for building stream-based processing pipelines. Furthermore, we offer an example implementing a TRNG from FM radio noise found between standard transmission bands.
A comprehensive performance analysis of a TRNG system that is based on beat frequency is provided in this paper for secure, high-entropy applications. The proposed method generates unpredictable bit streams by leveraging the intrinsic unpredictability caused by the frequency difference between two distinct oscillators. The system employs phase noise and variations in the surrounding environment to increase entropy without the need for laborious post-processing procedures. The study evaluates statistical variance using well-known test suites such as the NIST and Diehard tests to ensure high-quality output. The entropy rate, stability, power consumption, and throughput are among the critical performance metrics that are pertinent. The experimental data firmly refutes exogenous influences and deterministic patterns.
This study presents a new approach to generating true random numbers by exploiting the random nature inherent in mobile communication networks. Taking advantage of fluctuations in total users, user activity rate, and channel fading, we receive a raw signal in the radio spectrum using bowtie microstrip antennas designed for GSM and 5G frequency bands in the 1.8 and 3.5 GHz bands that can be used in our system. The captured data is then processed using a field-programmable gate array to generate high-quality true random numbers. Bow tie antennas are designed to have an omnidirectional pattern and can receive the signal from all directions. We have evaluated this through numerical calculations and antenna pattern measurements in the laboratory, and the results and their comparison show a promising performance. Our random number generation system also includes a Zynq SoC module and a dual-channel AD9226 ADC, enabling real-time random number generation through the ethernet port at up to 512Mbps. To ensure the system’s performance, standard statistical tests, including the NIST, DIEHARDER, and ENT test suites, have been done, and all confirm the true randomness of the generated random numbers. Our findings show the effectiveness of the 3.5 GHz frequency band as a suitable source for generating random numbers, especially in mobile devices and the Internet of Things. Due to the high speed of generating random numbers, this optimal and efficient TRNG system can have broad applications in cryptography, statistical analysis, and computational simulations.
Random numbers play a crucial role in cryptography since the security of cryptographic protocols relies on the assumption of the availability of uniformly distributed and unpredictable random numbers to generate secret keys, passwords, initialization vectors, nonces, salt, etc. True Random Number Generators (TRNGs) extract random numbers from physical processes (such as radioactive decay, thermal noise, and atmospheric noise) that are inherently unpredictable. However, real-world random number generators sometimes fail and produce outputs with low entropy, leading to security vulnerabilities. It is commonly observed that the TRNG outputs have statistical biases and dependencies that make them unsuitable to be directly used for cryptographic purposes. There are some standards and guidelines on generating and testing random numbers that are suitable for cryptographic applications. The National Institute of Standards and Technology (NIST) Special Publication (SP) 800-90 series provide guidelines and recommendations for generating random numbers for cryptographic applications and describes statistical randomness testing, estimating min-entropy with 10 black-box entropy estimation methods. In this thesis, we evaluate the effectiveness and limitations of the SP 800-90B methods by exploring the accuracy of these estimators using simulated random numbers with known entropy, investigating the correlation between entropy estimates, and studying the impacts of deterministic transformations on the estimators. To understand the unpredictability of the outputs, it is important to estimate their entropy accurately. The National Institute of Standards and Technology (NIST) Special Publication (SP) 800-90B specifies ten min-entropy estimators ranging from simple frequency-based estimators to more advanced approaches. Each estimator has specific assumptions, making them suitable for different types of sources. The minimum of these estimates is assumed to be the min-entropy of the TRNG. We propose a new entropy estimator (estimates min-entropy and also Shannon entropy) called index-value coincidence estimate that is suitable for outputs that might include some dependencies and we also provide some experimental results that demonstrate the effectiveness of the estimator. Additionally, TRNGs may be affected by outside conditions such as temperature, humidity, etc. Health tests are an integral part of the noise source of TRNG, defined to detect unexpected changes in the working process and dramatic changes in the amount of entropy generated by the noise source. Existing health test suites are examined, and a health test suite for cryptographic TRNGs is introduced by using random variables weight, run, runs of length 1 and overlapping templates. Some suggested parameters and experimental results are given.
Random numbers are important in diverse areas of science and technology. The requirements of randomness, however, are more stringent in cryptographic applications, where so-called true randomness is required. In particular, statistical randomness is not enough, and a degree of unpredictability stemmed in physical phenomenon is used to build a true random number generator (TRNG). A critical part in TRNG design is the entropy harvester, which extracts entropy from multiple entropy sources. Unfortunately, none of such sources provide true randomness in pure form, and the desired signal is heavily contaminated due to electrical coupling, electromagnetic interference, and other pseudo-random signals present in physical environment. Process variation and ageing affect the performance of noise sources. In this paper we propose an entropy extraction and estimation mechanism, which operates directly on raw randomness exhibited by analogue noise sources. The main strength of our design is its dynamic nature, i.e., it adapts as per the current state of source, and provides a robust mechanism to detect entropy failure. Our design can easily be implemented on commodity hardware in a cost effective manner.
Generators of random sequences used in high-end applications such as cryptography rely on entropy sources for their indeterminism. Physical processes governed by the laws of quantum mechanics are excellent sources of entropy available in nature. However, extracting enough entropy from such systems for generating truly random sequences is challenging while maintaining the feasibility of the extraction procedure for real-world applications. Here, we present a compact and an all-electronic van der Waals heterostructure-based device capable of detecting discrete charge fluctuations for extracting entropy from physical processes and use it for the generation of independent and identically distributed true random sequences. We extract a record-high value (>0.98 bits/bit) of min-entropy using the proposed scheme. We demonstrate an entropy generation rate tunable over multiple orders of magnitude and show the persistence of the underlying physical process for temperatures ranging from cryogenic to ambient conditions. We verify the random nature of the generated sequences using tests such as NIST SP 800-90B standard and other statistical measures and verify the suitability of our random sequence for cryptographic applications using the NIST SP 800-22 standard. The generated random sequences are then used in implementing various randomized algorithms without any preconditioning steps.
The key point of cryptography is cryptographic algorithms and keys. The random number generator is used to generate seeds and keys randomly in many cryptographic systems. For this reason, it is essential to use keys to encrypt and decrypt the transferring information, and the security of these keys is closely related to the security of 5G network. We propose a true random number generator (TRNG) method in this paper, and are using visible spectrum for noise source. We consider that if the cryptography system utilizes data of visible spectrum using the proposed TRNG, and the TRNG generates random numbers with high entropy.