Random Number Generators for Baseboard Management Controllers

Random number generation is a fundamental aspect of computer science, cryptography, simulations, and statistical sampling. This paper explores the definitions, classifications, and implementations of random and pseudo-random number generators (RNGs and PRNGs). We examine true random number generators (TRNGs), which derive randomness from physical phenomena, and pseudo-random number generators (PRNGs), which use deterministic algorithms to produce sequences that mimic randomness. Case studies, including Random.org, HotBits, laser-based RNGs, and the Linux random number generator, illustrate practical implementations. We also discuss vulnerabilities, security considerations, and the importance of entropy in generating unpredictable sequences.

Random numbers play a key-role in cryptography, since they are used, e.g., to define enciphering keys or passwords [1]. Nowadays, the generation of random numbers is obtained referring to two types of devices, that are often properly combined together: True Random Number Generators (TRNGs), and Pseudo Random Number Generators (PRNGs). The former are devices that exploit truly stochastic physical phenomena [2, 3, 4, 5, 6], such as the electronic noise or the chaotic dynamics of certain nonlinear systems: for these devices the output sequences have an intrinsic degree of unpredictability, that is measured referring to the theoretical tools provided by Information Theory (e.g., in terms of the Shannon entropy) [7,4]. On the other hand, PRNGs are deterministic periodic finite state machines whose aim is to emulate, within the period, the random behavior of a truly random source of numbers. From a theoretical point of view, due to their deterministic nature, PRNGs are potentially predictable by observing their generated sequences [8, 9, 10, 1]. Nevertheless, in literature some families of PRNGs are classified to be ‘secure’, meaning that their algorithmic structure involves calculations that in average, referring to the prediction task, require an amount of computation time that is asymptotically unfeasible with the size of the problem, when referring to both the computational equipment at disposal and the known computing fastest algorithms [1,11]. It is worth noting that a given generator, even if belonging to an asymptotically secure family of PRNGs, can generate short periodic (and unsecure) sequences for several values of the initial seed. Therefore, apart from the cryptographic robustness of their algorithmic structure, a cryptographic PRNG must generate sequences that are acceptable from a statistical point of view, i.e., that pass a certain number of standard statistical tests [1, 12].

Present day's data security plays a vital role in digital human life. Data is a valuable asset to any organization and hence its security from external attacks is very important. Information security is not only an important aspect but essential, to secure data from unapproved access. Data encryption, decryption and key management are the key factors in data protection. It is very important to have the right data security solution to meet the challenging threats. Cryptosystem implementation and random number generators are crucial for Cryptosystem applications such as security applications, space applications, military applications and smart cards et al. In this paper, we present the implementation of hybrid cryptosystem based on the True Random number Generator, pseudo Random number Generator and whitening the data by using the ASIC design flow.

The requirement of encryption is everywhere whether data is being transmitted or being stored in some devices. Random numbers can be used in data encryption. Every IoT device which is connected to a network needs secure and low cost techniques for encryption. In modern day the introduction of cryptocurrency wallet and other wallet services need more secure and random key for encryption which cannot be generated through algorithm-based Pseudo Random Number Generator (PRNG). The advanced video games require true random numbers. The connected portable IoT devices also transfer sensitive data. So, a low cost and simple circuit of True Random Number Generator (TRNG) effectuate the requirements. A non-deterministic signal or a noise signal is a source of true randomness. Detection of non-deterministic signal using sensors is a simple approach which is used in this paper to generate True Random Numbers. The TRNG proposed in this paper have simple hardware setup which includes an Arduino Uno board, LDR sensor and sound sensor. A simple modular equation and EXOR logic is used as processing algorithm. The random number generated through this setup passes all the tests of randomness given in the NIST-STS test.

We present a cascaded chaotic system as a hardware-efficient way of elevating the entropy in the chaotic behavior of CMOS-based chaotic maps. The chaotic performance of the proposed scheme is evaluated using the bifurcation plot, Lyapunov exponent, Kolmogorov entropy, and correlation coefficient. The improved entropy in the chaotic region benefits many security applications and is demonstrated experimentally in a new random number generator (RNG) design based on the proposed map. Unlike conventional mathematical chaotic map-based digital pseudo-random number generators (PRNG), the proposed design is not completely deterministic due to the high susceptibility of the core analog circuit to inevitable noise which renders this design closer to a true random number generator (TRNG). By leveraging the improved chaotic performance of the transistor-level cascaded map, significantly low area and power overhead are achieved in the RNG design and it passes three statistical tests namely, NIST, Diehard, and TestU01.

Random numbers are at the heart of diverse fields, ranging from simulations of stochastic processes to classical and quantum cryptography. The requirement for true randomness in these applications has motivated various proposals for generating random numbers based on the inherent randomness of quantum systems. The generation of true random numbers with arbitrarily defined probability distributions is highly desirable for applications, but it is very challenging. Here we show that single-photon quantum walks can generate multi-bit random numbers with on-demand probability distributions, when the required “coin” parameters are found with the gradient descent (GD) algorithm. Our theoretical and experimental results exhibit high fidelity for various selected distributions. This GD-enhanced single-photon system provides a convenient way for building flexible and reliable quantum random number generators. Multi-bit random numbers are a necessary resource for high-dimensional quantum key distribution.

The explosive growth of Internet of Things (IoT) devices has demanded the urgency of robust data security to protect private information. The random numbers generated by pseudo‐random number generators (PRNGs) play a crucial role in cryptographic algorithms. However, these sequences have become increasingly predictable due to advances in computing power and machine learning. Therefore, the development of true random number generators (TRNGs), which exploit the intrinsic hardware‐level randomness as an entropy source, is important for future data security. In this study, we present TRNG circuits that harness the inherent stochasticity of a multilayer hBN volatile memristor. The fabricated device exhibits highly stable threshold switching with low set/hold voltages and a significantly high on/off ratio. We implement a spike generator by integrating the threshold switching memristor with passive components. Digitization with a fixed‐reference comparator directly converts output spikes into bitstreams that successfully pass NIST randomness tests without postprocessing. The practical utility of the TRNG is demonstrated through XOR‐based encryption and decryption of black‐and‐white and grayscale images, resulting in noise‐like encrypted data and perfect recovery of the originals using identical keys. These results establish 2D hBN‐based threshold switching memristors as compelling hardware entropy sources for secure, next‐generation electronic systems.

In today’s world, advancement of Information Technology has been simultaneously followed by cyber crimes resulting in offensive and distressful digital contents. Threat to the digital content has initiated the need for application of forensic activities in digital field seeking evidence against any type of cyber crimes for the sake of reinforcement of the law and order. Digital Forensics is an interdisciplinary branch of computer science and forensic sciences, rapidly utilizing the recovery and/or investigation works on digital data explored in electronic memory based devices with reference to any cyber based unethical, illegal, and unauthorized activities. A typical digital forensic investigation work follows three steps to collect evidence(s): content acquisition, content analysis and report generation. In digital content analysis higher amount of data volumes and human resource(s) exposure to distressing and offensive materials are of major concerns. Lack of technological support for processing large amount of offensive data makes the analytical procedure quite time consuming and expensive. Thus, it results in a degradation of mental health of concerned investigators. Backlog in processing time by law enforcement department and financial limitations initiate huge demand for digital forensic investigators turning out trustworthy results within reasonable time. Forensic analysis is performed on randomly populated sample, instead of entire population size, for faster and reliable analysis procedure of digital contents. Present work reports about an efficient design methodology to facilitate random sampling procedure to be used in digital forensic investigations. Cellular Automata (CA) based approach has been used in our random sampling method. Equal Length Cellular Automata (ELCA) based pseudo-random pattern generator (PRPG) has been proposed in a cost effective manner utilizing the concept of random pattern generator. Exhibition of high degree randomness has been demonstrated in the field of randomness quality testing. Concerned cost effectiveness refers to time complexity, space complexity, design complexity and searching complexity. This research includes the comparative study for some well known random number generators, e.g., recursive pseudo-random number generator (RPRNG), atmospheric noise based true-random number generator (TRNG), Monte-Carlo (M-C) pseudo-random number generator, Maximum Length Cellular Automata (MaxCA) random number generator and proposed Equal Length Cellular Automata (ELCA) random number generator.Resulting sequences for all those above mentioned pattern generatorshave significant improvement in terms of randomness quality. Associated fault coverage is being improved using iterative methods. Emphasis on cost effectiveness has been initiated for proposed random sampling in forensic analysis.

We demonstrate a 6 Gbps real-time optical quantum random number generator by measuring vacuum fluctuation. To address the common problem that speed gap exists between fast randomness generation and slow randomness extraction in most high-speed real-time quantum random number generator systems, we present an optimized extraction algorithm based on parallel implementation of Toeplitz hashing to reduce the influence of classical noise due to the imperfection of devices. Notably, the real-time rate of randomness extraction we have achieved reaches the highest speed of 12 Gbps by occupying less computing resources, and the algorithm has the ability to support hundreds of Gbps randomness extraction. By assuming that the eavesdropper with complete knowledge of the classical noise, our generator has a randomness generation speed of 6.83 Gbps and this supports the generation of 6 Gbps information-theoretically provable quantum random numbers, which are output in real-time through peripheral component interconnect express interface.

Random number generators (RNG) are essential elements in many cryptographic systems. True random number generators (TRNG) rely upon sources of randomness from natural processes such as those arising from quantum mechanics phenomena. We demonstrate that a quantum computer can serve as a high-quality, weakly random source for a generalized user-defined probability mass function (PMF). Specifically, QC measurement implements the process of variate sampling according to a user-specified PMF resulting in a word comprised of electronic bits that can then be processed by an extractor function to address inaccuracies due to non-ideal quantum gate operations and other system biases. We introduce an automated and flexible method for implementing a TRNG as a programmed quantum circuit that executes on commercially-available, gate-model quantum computers. The user specifies the desired word size as the number of qubits and a definition of the desired PMF. Based upon the user specification of the PMF, our compilation tool automatically synthesizes the desired TRNG as a structural OpenQASM file containing native gate operations that are optimized to reduce the circuit's quantum depth. The resulting TRNG provides multiple bits of randomness for each execution/measurement cycle; thus, the number of random bits produced in each execution is limited only by the size of the QC. We provide experimental results to illustrate the viability of this approach.

Quantum random number generators (QRNG) represent an advanced solution for randomness generation, which is essential in every cryptographic application. In this context, integrated arrays of single-photon detectors have promising applications as QRNGs based on the spatial detection of photons. For the employment of QRNGs in cryptography, it is necessary to have efficient methods to evaluate the so-called quantum min-entropy that corresponds to the amount of the true extractable quantum randomness from the QRNG. Here, we present an efficient method that allows the estimation of the quantum min-entropy for a multi-detector QRNG. In particular, we consider a scenario in which an attacker can control the efficiency of the detectors and knows the emitted number of photons. Eventually, we apply the method to a QRNG with 103 detectors.

True random number generators (TRNG) are important counterparts to pseudorandom number generators (PRNG), especially for high security applications such as cryptography. They produce unpredictable, non-repeatable random sequences. However, most TRNGs require specialized hardware to extract entropy from physical phenomena and tend to be slower than PRNGs. These generators usually require post-processing algorithms to eliminate biases but in turn, reduces performance. In this paper, a new post-processing method based on hyperchaos is proposed for software-based TRNGs which not only eliminates statistical biases but also provides amplification in order to improve the performance of TRNGs. The proposed method utilizes the inherent characteristics of chaos such as hypersensitivity to input changes, diffusion, and confusion capabilities to achieve these goals. Quantized bits of a physical entropy source are used to perturb the parameters of a hyperchaotic map, which is then iterated to produce a set of random output bits. To depict the feasibility of the proposed post-processing algorithm, it is applied in designing TRNGs based on digital audio. The generators are analyzed to identify statistical defects in addition to forward and backward security. Results indicate that the proposed generators are able to produce secure true random sequences at a high throughput, which in turn reflects on the effectiveness of the proposed post-processing method.

Random numbers are widely used in various applications. In the majority of cases, a pseudo-random number generator is used since true random number generators are slow and they are barely suitable for the hardware implementation. In this paper, we present new architecture of URNG (uniform random number generator) employing Leap-Ahead LFSR architecture for hardware implementation. In particular, the proposed URNG consists of two more segmented Leap-Ahead LFSRs to overcome the drawback of the conventional URNG employing Leap-Ahead architecture, that is, the sharp decrease of a maximum period of the generated random numbers. Thus, the proposed URNG with segmented LFSR architecture can generate multiple bits random number in a cycle without the frequent diminishing of maximum period of the generated random numbers. We prove the efficiency of the proposed segmented LFSR-architecture through the mathematical analysis. The simulation results show that the proposed URNG employing segmented Leap-Ahead LFSR architecture can be increased 2.5 times of the maximum period of generated random numbers compared to the URNG using the conventional Leap-Ahead architecture.

Pseudo random number generators (PRNG) play a key role in various security and cryptographic applications where the performance of these applications is directly related to the quality of generated random numbers. The design of such random number generators is a challenging task. In this paper, we propose an application of Hopfield Neural Networks (HNN) as pseudo random number generator. This is done based on a unique property of HNN, i.e., its unpredictable behavior under certain conditions. We compare the main features of ideal random number generators with those of PRNG based on Hopfield Neural Networks. We use a battery of statistical tests developed by National Institute of Standards and Technology (NIST) to measure the performance, and to evaluate the quality of the proposed Hopfield random number generator.

A true random number generator (TRNG) is essential to ensure information security for Internet of Things (IoT) edge devices. While pseudorandom number generators (PRNGs) have been instrumental, their deterministic nature limits their application in security-sensitive scenarios. In contrast, hardware-based TRNGs derived from physically unpredictable processes offer greater reliability. This study demonstrates a peripheral-free TRNG utilizing two cascaded three-stage inverters (TSIs) in conjunction with an XOR gate composed of monolayer molybdenum disulfide (MoS2) field-effect transistors (FETs) by exploiting the stochastic charge trapping and detrapping phenomena at and/or near the MoS2/dielectric interface. The entropy source passes the NIST SP800-90B tests with a minimum normalized entropy of 0.8780, while the generated bits pass the NIST SP800-22 randomness tests without any postprocessing. Moreover, the keys generated using these random bits are uncorrelated with near-ideal entropy, bit uniformity, and Hamming distances, exhibiting resilience against machine learning (ML) attacks, temperature variations, and supply bias fluctuations with a frugal energy expenditure of 30 pJ/bit. This approach offers an advantageous alternative to conventional silicon, memristive, and nanomaterial-based TRNGs as it obviates the need for extensive peripherals while harnessing the potential of atomically thin 2D materials in developing low-power TRNGs.