Implementing　hardware　primitives　into　cryptosystem　has　become　a　new　trend　in　electronic　community.　Memristor,　with　intrinsic　stochastic　characteristics　including　the　switching　voltages,　times　and　energies,　as　well　as　the　fluctuations　of　the　resistance　state　over　time,　could　be　a　naturally　good　entropy　source　for　cryptographic　key　generation.　In　this　study,　based　on　kinetic　Monte　Carlo　Simulation,　multiple　Artificial　Intelligence　techniques,　as　well　as　kernel　density　map　and　time　constant　analysis,　memristive　spatiotemporal　variability　within　graphene　based　conductive　bridging　RAM　(CBRAM)　have　been　synergistically　analyzed　to　verify　the　inherent　randomness　of　the　memristive　stochasticity.　Moreover,　the　random　number　based　on　hardware　primitives　passed　the　Hamming　Distance　calculation　with　high　randomness　and　uniqueness,　and　has　been　integrated　into　a　Rivest-Shamir-Adleman　(RSA)　cryptosystem.　The　security　of　the　holistic　cryptosystem　relies　both　the　modular　arithmetic　algorithm　and　the　intrinsic　randomness　of　the　hardware　primitive　(to　be　more　reliable,　the　random　number　could　be　as　large　as　possible,　better　larger　than　2048　bits　as　NIST　suggested).　The　spatiotemporal-variability-based　random　number　is　highly　random,　physically　unpredictable　and　machine-learning-attack　resilient,　improving　the　robustness　of　the　entire　cryptosystem.　©　2023