Optimization of Acoustic Entropy Source for Random Sequence Generation Using an Improved Grey Wolf Algorithm

Abstract

The functionality of cryptographic systems necessitates unpredictable, high-quality random numbers. High-quality random numbers must possess unpredictability, non-reproducibility, and strong statistical properties. To achieve these qualities, True Random Number Generators (TRNG) are employed. The randomness quality of TRNG-derived sources depends on the entropy source used. Physical noise sources, ring oscillators, metastable, acoustic sources, and chaotic attractors are commonly used as entropy sources. In recent years, the use of acoustic signals as entropy sources has attracted attention. However, the noise in the signals affects the bit sequence to be generated. In addition, the threshold and sampling interval applied to the frequency values obtained from the signals also determine the quality of the bit sequence to be produced. Choosing the most appropriate values for the entropy source, randomness and unpredictability of these values are important for the bit sequence to have good statistical properties and to be used in strong cryptographic applications. In this paper, a swarm intelligence-based approach is proposed to determine the optimal threshold and sampling interval by exploiting the power of randomness and unpredictability such as random initialization of swarm intelligence algorithms and obtaining different optimal solutions in each run. In the proposed approach, a bit sequence is generated by applying the values determined by the Improved Grey Wolf Optimization algorithm on the data taken from the MUSDB18 dataset as an entropy source. The generated bit sequences have been shown to be usable as initial value, seed value or additional input for cryptographic key and random number generators by obtaining a p-value greater than 0.01 from the National Institute of Standards and Technology (NIST) test, statistical complexity test (SCM) and autocorrelation test with results close to 0 by obtaining 0.013, 0.074 respectively. Furthermore, to show that the obtained bit sequences can be used as cryptographic keys in the encryption system, we perform encryption on two different images and present histogram and differential attack (UACI, NPCR) test results.