AI-Hybrid TRNG: Kernel-Based Deep Learning for Near-Uniform Entropy Harvesting from Physical Noise
This work addresses the need for secure and deployable random number generators across cryptographic systems and resource-constrained devices, representing a novel hybrid approach rather than an incremental improvement.
The paper tackles the problem of generating high-entropy random numbers without expensive hardware by proposing a deep-learning framework that extracts near-uniform entropy from physical noise using a low-cost RF front end and CPU-timing jitter, achieving results that pass NIST SP 800-22 and other statistical tests with a model footprint below 0.5 MB.
AI-Hybrid TRNG is a deep-learning framework that extracts near-uniform entropy directly from physical noise, eliminating the need for bulky quantum devices or expensive laboratory-grade RF receivers. Instead, it relies on a low-cost, thumb-sized RF front end, plus CPU-timing jitter, for training, and then emits 32-bit high-entropy streams without any quantization step. Unlike deterministic or trained artificial intelligence random number generators (RNGs), our dynamic inner-outer network couples adaptive natural sources and reseeding, yielding truly unpredictable and autonomous sequences. Generated numbers pass the NIST SP 800-22 battery better than a CPU-based method. It also passes nineteen bespoke statistical tests for both bit- and integer-level analysis. All results satisfy cryptographic standards, while forward and backward prediction experiments reveal no exploitable biases. The model's footprint is below 0.5 MB, making it deployable on MCUs and FPGA soft cores, as well as suitable for other resource-constrained platforms. By detaching randomness quality from dedicated hardware, AI-Hybrid TRNG broadens the reach of high-integrity random number generators across secure systems, cryptographic protocols, embedded and edge devices, stochastic simulations, and server applications that need randomness.