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arXiv:2409.05543 · IEEE International Conference on Cyber Security and Resilience (CSR)

On-line Anomaly Detection and Qualification of Random Bit Streams

Cesare Gerolimetto Fabrello, Valeria Rossi, Kamil Witek, Alberto Trombetta, M. Caccia

Published Sep 2, 2024• 2 citations
arXivHTMLPDF

Abstract

Generating random bit streams is required in various applications, most notably cyber-security. Ensuring high-quality and robust randomness is crucial to mitigate risks asso-ciated with predictability and system compromise. True random numbers provide the highest unpredictability levels. However, potential biases in the processes exploited for the random number generation must be carefully monitored. This paper reports the implementation and characterization of an on-line 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. The procedure is firmware implemented and performed simultaneously with the bit stream generation, and providing as well an estimate of the entropy of the source. The experimental validation of the approach is performed upon the bit streams generated by a quantum, silicon-based entropy source.

DOI
10.1109/csr61664.2024.10679431

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Generating random bit streams is required in various applications, most notably cyber-security. Ensuring high-quality and robust randomness is crucial to mitigate risks asso-ciated with predictability and system compromise. True random numbers provide the highest unpredictability levels. However, potential biases in the processes exploited for the random number generation must be carefully monitored. This paper reports the implementation and characterization of an on-line 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. The procedure is firmware implemented and performed simultaneously with the bit stream generation, and providing as well an estimate of the entropy of the source. The experimental validation of the approach is performed upon the bit streams generated by a quantum, silicon-based entropy source.
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.
Availability of streams of random numbers is critical in a number of significant applications. e.g. computer security and cryptography, privacy preservation procedures,IoT\mathbf{IoT}IoTsecure communication, numerical simulation of complex phenomena, gaming and gambling. Hardware generation of random numbers, especially when based on quantum phenomena, is made unbreakable by the same laws of nature. Random Power [1] [2] is focusing on the development of a Quantum-True Random Number generation (QTRNG) platform, producing unpredictable bit streams analyzing the time series of self-amplified endogenous pulses due to stochastically generated charge carriers in a dedicated silicon device. As per NIST recommendations [3], a SHA256 conditioning function was used in order to reduce potential biases and guarantee the required level of entropy. This paper reports the firmware development and implementation of a real-time SHA256 accelerator, which is capable of generating bits in bursts at a maximum rate of 330 Mbps.
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We demonstrate a random bit streaming system that uses a chaotic laser as its physical entropy source. By performing real-time bit manipulation for bias reduction, we were able to provide the memory of a personal computer with a constant supply of ready-to-use physical random bits at a throughput of up to 4 Gbps. We pay special attention to the end-to-end entropy source model describing how the entropy from physical sources is converted into bit entropy. We confirmed the statistical quality of the generated random bits by revealing the pass rate of the NIST SP800-22 test suite to be 65 % to 75 %, which is commonly considered acceptable for a reliable random bit generator. We also confirmed the stable operation of our random bit steaming system with long-term bias monitoring.
Random number generation is significant for secure communication, data security and cryptography. However, while Quantum Random Number Generators (QRNGs) rely on quantum uncertainty, environmental data can also serve as practical sources of randomness for true random number generation. Environmental data such as temperature, humidity, and wind speed exhibit continuous variability over time; these changes arise from complex weather behavior. In this paper, temperature data collected from two meteorological stations in Pakistan, Karachi and Hyderabad, are used to generate random bits. Everyday temperature values are transformed into binary sequences using a mean-based thresholding technique, followed by post-processing with the Von Neumann extractor to decrease bias and correlation. The quality of the generated random bits is evaluated using Shannon entropy, lossless compression testing with the Gzip, Bzip2, and LZMA algorithms, the NIST SP 800-22 statistical test suite and the auto-correlation analysis. The results determine that correctly processed temperature-based entropy can produce statistically usable random sequences suitable for randomness testing and security-related research.
Random number generators (RNGs) are foundational to modern cryptographic systems.While quantum random number generators (QRNGs) leverage inherently stochastic quantum measurements, practical deployments still exhibit implementation artifacts (e.g., detector dead time, afterpulsing, drift) that can introduce bias, correlation, and non-stationarity.This paper presents a unified validation framework for both CMOS true random number generators (TRNGs) and QRNGs under NIST SP 800-90B entropy source validation.Rather than simulating quantum physics, we model observable raw-output behaviors of practical implementations and evaluate them using a consistent pipeline: raw capture constraints, IID screening, conservative min-entropy bounding, and online health testing (repetition count test and adaptive proportion test).Using synthetic sources that emulate typical CMOS and QRNG failure modes, we demonstrate how similar artifacts drive the IID versus non-IID decision and tighten entropy bounds, motivating a vendor-agnostic, reproducible validation methodology.