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IEEE International Black Sea Conference on Communications and Networking

Cryptographically Resilient Random Number Generators based on Entropy Sources in Information Security Systems

Юлія Володимирівна КОСТЮК, Volodymyr Yu. Sokolov, Pavlo Skladannyi, Ievgeniia Kuzminykh, Bogdan Ghita

Published Jun 8, 2026• 0 citations
Publisher

Abstract

The paper proposes a hybrid entropy-controlled random number generator for information security systems that combines physical entropy sources, cryptographic conditioning, and adaptive feedback control. A comparative analysis of traditional pseudo-random and quantum generators was conducted, and experimental testing was performed in accordance with NIST SP 800-90B, NIST SP 800-90C, and ISO/IEC 18031:2025. According to statistical analysis of a large volume of bit sequences, minimum entropy close to unity was observed, with only a slight deviation, confirming the high randomness. The throughput of the hardware implementation is ~ 600 Mbit/s (over 500 Mbit/s) with an energy consumption of E ≈ 0.29 mJ/Mbit. Compared to classical generators on an uncontrolled cypher counter or hardware generators on ring oscillators without pre-mixing, a reduction in correlation and mean shift of more than a third has been achieved. The proposed architecture demonstrates resistance to source manipulation, fault injection, and side-channel attacks, confirming the effectiveness of the entropy approach for modern cryptographic systems, Internet of Things platforms, and security modules.

DOI
10.1109/blackseacom69760.2026.11598257

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The paper proposes a hybrid entropy-controlled random number generator for information security systems that combines physical entropy sources, cryptographic conditioning, and adaptive feedback control. A comparative analysis of traditional pseudo-random and quantum generators was conducted, and experimental testing was performed in accordance with NIST SP 800-90B, NIST SP 800-90C, and ISO/IEC 18031:2025. According to statistical analysis of a large volume of bit sequences, minimum entropy close to unity was observed, with only a slight deviation, confirming the high randomness. The throughput of the hardware implementation is ~ 600 Mbit/s (over 500 Mbit/s) with an energy consumption of E ≈ 0.29 mJ/Mbit. Compared to classical generators on an uncontrolled cypher counter or hardware generators on ring oscillators without pre-mixing, a reduction in correlation and mean shift of more than a third has been achieved. The proposed architecture demonstrates resistance to source manipulation, fault injection, and side-channel attacks, confirming the effectiveness of the entropy approach for modern cryptographic systems, Internet of Things platforms, and security modules.
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.
Generating random bit streams is required in various applications, most notably in cyber-security, which is essential for Internet of Everything applications to enable secure communication between interconnected devices. Ensuring high-quality and robust randomness is crucial to mitigate risks associated with predictability and system compromise. True random numbers provide the highest levels of unpredictability. However, known systematic biases that can emerge from physical imperfections, environmental variations, and device aging in the processes exploited for random number generation must be carefully monitored. This article reports the implementation and characterization of an online procedure for the detection of anomalies in a true random bit stream. It is based on the NIST adaptive proportion and repetition count tests, complemented by statistical analysis relying on the Monobit and RUNS tests. The procedure is implemented in firmware through dedicated hardware accelerators processing configurable-length sequences, with automated anomaly detection triggering alerts after three consecutive threshold violations. The implementation is performed simultaneously with bit stream generation and also provides an estimate of the entropy of the source. A statistical analysis of the results from the NIST procedure to evaluate the symbols of the bit-stream as independently and identically distributed is also performed, leading to a computation of the minimum entropy of the source that cross-checks the previously mentioned estimate. The experimental validation of the approach is performed up the bit streams generated by a quantum, silicon-based entropy source.
Random number generators play a pivotal role in generating security primitives, e.g., encryption keys, nonces, initial vectors, and random masking for side-channel countermeasures. A quantum entropy source based on radioactive isotope decay can be exploited to generate random numbers with sufficient entropy. If a deterministic random bit generator (DRBG) is combined for post-processing, throughput of the quantum random number generator (QRNG) can be improved. However, general DRBGs are susceptible to side-channel attacks. In this paper, we propose a framework called SCR-QRNG framework, which offers Side-Channel Resistant primitives using QRNG. The QRNG provides sources of randomness for modulating the clock frequency of a DRBG to obfuscate side-channel leakages, and to generate unbiased random numbers for security primitives. The QRNG has robustness against power side-channel attacks and is in compliance with NIST SP 800-22/90B and BSI AIS 31. We fabricate a quantum entropy chip, and implement a PCB module for a random frequency clock generator and a side-channel resistant QRNG on an FPGA.
The reliability and security of telecommunication systems depend on the quality of random number generators (RNGs). Quantum Random Number Generators (QRNGs) enhance randomness over classical methods, improving cryptographic strength. This paper evaluates three QRNG implementations using IBM quantum computers, analyzing their methodologies, performance, and limitations. We introduce a new QRNG algorithm on the IBM Quantum Experience (IBMQ) platform, using real quantum computers instead of simulators, and incorporating post-processing with XOR bit masking. Our algorithm achieves entropy 0.86458 and passes more NIST 800-90B and NIST 800–22 randomness tests, demonstrating its effectiveness and reliability. This research supports secure telecommunication technologies by providing a reliable source of true randomness.
One of the solutions to the problem of secure communication is the Quantum Random Number Generators (QRNGs) which generate true randomness based on quantum mechanical principles.QRNGs are different from conventional pseudo random number generator in that they use quantum uncertainty to generate outputs which are very unpredictable.This work attempts to examine different QRNG architectures, study the possible integration of cryptographic frameworks with these architectures and highlight potential vulnerabilities in the integration of cryptographic frameworks with QRNG architectures.Post-processing techniques, i.e.Zero-Phase Component Analysis (ZPCA) and neural network based techniques are also studied to increase entropy and suppress environmental noise in QRNG systems.Key findings show that system jitter models based QRNG architectures are more scalable and less expensive for use in real world applications.Additionally, incorporating cryptographic protocols with the QRNGs endows them with the ability to resist quantum threats, and hence, provide better key distribution in the secure IoT communication frameworks.However, as security problems such as side channel attacks and hardware manipulation are still big issues.It also points out some of the emerging research trends including hybrid QRNG architectures, i.e., QRNG with classical PRNG to increase the entropy rate.As such, the proposed methodologies attempt to enhance QRNG scalability, security, and energy efficiency in cloud infrastructures, in mobile systems and in critical security environments.The advancement of these technologies will assume a crucial function in the advancement of quantum-safe frames for current advanced computerized foundation.
Random-bit generators (RBGs) are key components of a variety of information processing applications ranging from simulations to cryptography. In particular, cryptographic systems require ``strong'' RBGs that produce high-entropy bit sequences, but traditional software pseudo-RBGs have very low entropy content and therefore are relatively weak for cryptography. Hardware RBGs yield entropy from chaotic or quantum physical systems and therefore are expected to exhibit high entropy, but in current implementations their exact entropy content is unknown. Here we report a quantum random-bit generator (QRBG) that harvests entropy by measuring single-photon and entangled two-photon polarization states. We introduce and implement a quantum tomographic method to measure a lower bound on the ``min-entropy'' of the system, and we employ this value to distill a truly random-bit sequence. This approach is secure: even if an attacker takes control of the source of optical states, a secure random sequence can be distilled.