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Leveraging the Stochastic Nature of Solid-State Nanopore Sensing for Random Number Generation: A Step Towards Cybersecurity Engineering

Katherine Gussenhoven, Dhanush Lakmal Yapa Amarasekara, Teesha Pathirage, Jayamini Hewage, Garrett Bradshaw, Y. M. Nuwan D. Y. Bandara

Published Jun 18, 2026• 0 citations
Publisher

Abstract

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.

DOI
10.26434/chemrxiv.15004933/v1

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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.
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.
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.
In the ever-evolving field of cybersecurity, randomness plays a critical role in cryptographic systems. Traditional pseudo-random number generators, while widely used, can be vulnerable to attacks due to their deterministic nature. This paper explores the integration of quantum randomness into SSH authentication by utilizing a Quantum Random Number Generator (QRNG) to enhance key generation across various asymmetric cryptographic algorithms, specifically RSA, ECDSA, and EdDSA. Quantum-generated randomness offers a higher level of unpredictability, strengthening the security of cryptographic keys. We propose a method to generate keys using quantum random bits, hash them using SHA-256, and secure them for SSH authentication. The generated keys are evaluated for security improvements over conventional methods, and the performance of SSH authentication is analyzed. The results demonstrate that incorporating quantum randomness into key generation increases the cryptographic strength of the keys, offering a more robust defense against potential cryptanalytic attacks, while maintaining compatibility with existing SSH protocols. This approach signifies a step forward in leveraging quantum technologies to enhance network communication security.
Cryptographic applications need high-quality random number generator (RNG) for strong security and privacy measures. This paper presents RNG based on a hydrogen gas sensor that is fabricated by using microfabrication techniques. The proposed approach extracts the thermal noise information as an entropy source from the gas sensor that is non-deterministic during its operation and using hash function SHA-256 as post processing. This non-deterministic noise is then processed to acquire a random number set fulfilling the NIST 800-22 statistical randomness test suite and it demonstrates that a gas sensor based RNG can provide high-quality random numbers. Secure data transfer is possible by having this method directly without any other hardware where hydrogen gas sensor needs to be used such as petrochemical field, fuel cells, and nuclear reactors.
Secure data transmission depends on unpredictable cryptographic keys. Conventional pseudorandom number generators are deterministic and vulnerable to prediction. This work proposes a Quantum True Random Number Generator (Q-TRNG) that uses quantum superposition and noise to produce truly random numbers. The generated quantum entropy is converted into high-quality random bits and applied to secure encryption, improving confidentiality and resistance to attacks. The proposed system offers a reliable and future- ready solution for quantum-grade secure communication.
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.