Abstract
In this paper, a deep learning (DL)-based predictive analysis is proposed to analyze the security of a non-deterministic random number generator (NRNG) using white chaos. In particular, the temporal pattern attention (TPA)-based DL model is employed to learn and analyze the data from both stages of the NRNG: the output data of a chaotic external-cavity semiconductor laser (ECL) and the final output data of the NRNG. For the ECL stage, the results show that the model successfully detects inherent correlations caused by the time-delay signature. After optical heterodyning of two chaotic ECLs and minimal post-processing are introduced, the model detects no patterns among corresponding data. It demonstrates that the NRNG has the strong resistance against the predictive model. Prior to these works, the powerful predictive capability of the model is investigated and demonstrated by applying it to a random number generator (RNG) using linear congruential algorithm. Our research shows that the DL-based predictive model is expected to provide an efficient supplement for evaluating the security and quality of RNGs.
Highlights
Random number generators (RNGs) are extensively applied in the field of cryptography and security communications that require fast and trusted random numbers [1]
For security analysis of the non-deterministic random number generator (NRNG), we investigate the quality of data collected from the output of the ECL1 and the final output of the NRNG
We investigate the security of datasets extracted at different stages of the NRNG based on white chaos from the perspective of deep learning (DL)
Summary
Random number generators (RNGs) are extensively applied in the field of cryptography and security communications that require fast and trusted random numbers [1]. Truong et al [18] developed a recurrent convolutional neural network (RCNN)-based predictive model, which detected prominent inherent correlations of deterministic noise sources in a quantum random number generator. DL has promising applications in evaluating the quality of random sequences, there are few studies on the security analysis for NRNGs based on white chaos by deep learning. A DL-based predictive analysis is proposed to analyze the security of RNGs. In particular, the temporal pattern attention (TPA)-based DL model is employed to detect hidden correlations that may exist among the long random sequence from RNGs, and predict the random number, based on observed random numbers in an input sequence. We investigate the reasons behind the advantage provided by DL
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