The stochastic nature of photocurrent generation can be used as a source of entropy for various applications in the field of cybersecurity. Cybersecurity software solutions in the field of software engineering, particularly steganography, require random number sequences (RNS) in their algorithms. Standard methods provided by frameworks and programming languages often fail to ensure sufficient randomness. Physical devices measuring stochastic processes, such as beta radiation counters, can provide true unpredictability but are impractical to maintain. As an alternative, a photodiode can be used as an RNS source by measuring its photocurrent. Experiments under near-total darkness (0.1 lux) and uniform illumination (200 lux) confirmed that the current values follow a Gaussian distribution and demonstrate high unpredictability within a defined range. By means of software engineering, a set of continuous photocurrent measurements can be transformed into a sequence of integer random numbers following a Poisson distribution. An experimental setup was developed to investigate this hypothesis and to evaluate the quality of the generated sequences with respect to cybersecurity requirements. This approach enables the development of a cost-effective, accessible, and cryptographically secure hardware random number generator without relying on bulky or expensive equipment.
The stochastic nature of photocurrent generation can be used as a source of entropy for various applications in the field of cybersecurity. Cybersecurity software solutions in the field of software engineering, particularly steganography, require random number sequences (RNS) in their algorithms. Standard methods provided by frameworks and programming languages often fail to ensure sufficient randomness. Physical devices measuring stochastic processes, such as beta radiation counters, can provide true unpredictability but are impractical to maintain. As an alternative, a photodiode can be used as an RNS source by measuring its photocurrent. Experiments under near-total darkness (0.1 lux) and uniform illumination (200 lux) confirmed that the current values follow a Gaussian distribution and demonstrate high unpredictability within a defined range. By means of software engineering, a set of continuous photocurrent measurements can be transformed into a sequence of integer random numbers following a Poisson distribution. An experimental setup was developed to investigate this hypothesis and to evaluate the quality of the generated sequences with respect to cybersecurity requirements. This approach enables the development of a cost-effective, accessible, and cryptographically secure hardware random number generator without relying on bulky or expensive equipment.
Random numbers (RNs) have applications in many areas: lottery games, gambling, computer simulation, and, most importantly, cryptography [N. Gisin et al., Rev. Mod. Phys. 74 (2002) 145]. In cryptography theory, the theoretical security of the system calls for high quality RNs. Therefore, developing methods for producing unpredictable RNs with adequate speed is an attractive topic. Early on, despite the lack of theoretical support, pseudo RNs generated by algorithmic methods performed well and satisfied reasonable statistical requirements. However, as implemented, those pseudorandom sequences were completely determined by mathematical formulas and initial seeds, which cannot introduce extra entropy or information. In these cases, “random” bits are generated that are not at all random. Physical random number generators (RNGs), which, in contrast to algorithmic methods, are based on unpredictable physical random phenomena, have attracted considerable research interest. However, the way that we extract random bits from those physical entropy sources has a large influence on the efficiency and performance of the system. In this manuscript, we will review and discuss several randomness extraction schemes that are based on radiation or photon arrival times. We analyze the robustness, post-processing requirements and, in particular, the extraction efficiency of those methods to aid in the construction of efficient, compact and robust physical RNG systems.
Randomness occurs in every kind of application in which random events should be experienced such as gambling, statistical calculations, computer simulations, information security and encryption. The quality of randomness is very important for the events using randomness. In particular, the need for random numbers used in the field of cryptology to be unpredictable increases the importance of random number generators. \n \nRandom Number Generators (RNGs) are software or hardware components that can be used to generate numbers that cannot be predicted in any pattern or relationship. Software-based RNGs are deterministic applications, while hardware-based RNGs are the devices that can be integrated into the system. \n \nEncryption using numbers generated by software-based RNG can be broken in varying times depending on the strength of the encryption because it was developed in deterministic ways. It is expected that the decoding time will be shortened considerably by the quantum computers which are tried to be developed today besides classic computers that takes a long time to analyze. The hardware RNGs are more powerful in randomness quality than software-based generators, but cost-effective and use-limited generators. \n \nQuantum Random Number Generators (QRNGs) is a kind of generator based on quantum physics laws instead of classical physics. In photonic based QRNGs are produced the random numbers after various software and hardware operations by taking advantage of the uncertainty of photons. These generated numbers are unpredictably strong random numbers. \n \nIn this study, a QRNG was obtained by using the RGB (Red-Green-Blue) values of the images taken from a white-single LED light source and the randomness tests of the produced numbers were carried out with the tests provided by NIST (National Institute of Standards and Technology). As a result, the numbers derived from the images successfully passed NIST tests and true random number sequences were obtained.
Unpredictability and entropy are key factors governing the success of cryptographic security, and true random numbers play a pivotal role in this space. While software-driven random numbers are easy and efficient to generate, their deterministic nature creates a vulnerability that could compromise the gate, barring entry to a sensitive system. Here, we propose using measurement randomness arising from physical and chemical aspects of transport (Layer 1), hardware characteristics (Layer 2), event features and extraction (Layer 3; facilitated by the HyperXtract 2.0), and cryptographic whitening via the secure hash algorithm (SHA-256; Layer 4) to create a high-entropy TRNG system. We demonstrate a host of unique experimental conditions (e.g., electrolyte chemistry, applied voltage, analyte type, transport direction, temperature, and amplifier type) across these four layers to generate ~480,000 256-bit long keys with 100% uniqueness in a Pearson-correlation-based statistical entropy harvesting method and most other methods reporting >99.9% unique keys in most instances. Furthermore, the randomness of these keys was evaluated using the NIST SP800-22b statistical test suite and yielded a success rate of ~99% at a 1% significance level. This study thus showcases the utility of solid-state nanopore sensing for generating high-entropy TRNGs and the potential as a robust, non-deterministic source for nextgeneration cybersecurity engineering.
One key requirement for many cryptograhic schemes is the generation of random numbers.Sequences of random numbers are used at several stages of a standard cryptographic protocol.One simple example is a Vernam cipher, where a string of random numbers is added to message string to generate encrypted code.C = M ⊕ K.It has been mathematically shown that this simple scheme is unbreakable if key K is as long as M and is used only once.The security of a cryptosystem shall not be based on keeping the algorithm secret but solely on keeping the key secret.The security of a random number generator (RNG) is related to the difficulty of predicting its future sequence values from past values.The quality and unpredictability of secret data is critical to securing communication by modern cryptographic techniques.The generation of such data for cryptographic purposes typically requires an unpredictable physical source of random data.We studied a chaotic circuit which consisted of an inductor, capacitance, diode and thus used for the BB84 protocol.We have studied both pseudo random and true random number generators and evaluated them through various tests like frequency, correlation, NIST etc.
Topicality. Reliable random number generation is one of the key requirements for modern cryptographic systems and cybersecurity applications. Traditional pseudo-random number generators are based on deterministic algorithms and may exhibit limited entropy and predictability, which can negatively affect information security. The subject of study in the article is methods of quantum random number generation and approaches to evaluating the quality of generated random sequences. The purpose of the article is to analyze modern methods of quantum random number generation, develop a software tool for obtaining quantum random numbers based on the ANU Quantum Random Number Generator service, perform statistical testing of the generated sequences, and analyze the obtained results. The following results were obtained. Modern quantum random number generation systems were analyzed and compared. A software tool was implemented in Python for automatic retrieval of quantum random numbers through the ANU QRNG API. The generated binary sequences were evaluated using Frequency Test and Block Frequency Test from the NIST SP 800-22 statistical test suite. The obtained p-values were 0.977181 and 0.200659 respectively, confirming successful completion of both tests. Additionally, visual analysis of the generated sequences revealed no noticeable patterns or repetitive structures. Conclusion. The obtained results confirm the high quality of quantum random sequences generated using ANU QRNG and demonstrate the applicability of the developed software tool for cryptographic and cybersecurity applications requiring high-quality randomness.
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.