Generating random bit streams is required in various applications, most notably cyber-security. Ensuring high-quality and robust randomness is crucial to mitigate risks associated 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.

翻译：生成随机比特流在众多应用领域中具有重要需求，尤其在网络安全领域。确保高质量且稳健的随机性对于降低可预测性及系统安全风险至关重要。真随机数能够提供最高级别的不可预测性。然而，必须对随机数生成过程中可能存在的偏差进行严格监控。本文报道了一种用于真随机比特流异常检测的在线方法的实现与特性分析。该方法基于NIST自适应比例测试与重复计数测试，并辅以依赖于单比特测试与游程测试的统计分析。该流程通过固件实现，可与比特流生成过程同步执行，同时提供信源熵的估计值。该方法的实验验证基于量子硅基熵源所产生的比特流进行。