The　development　of　physical-level　primitives　for　cryptographic　applications　has　emerged　as　a　trend　in　the　electronic　community,　while　the　methods　for　protecting　the　generators　from　counterfeiting　have　yet　to　be　explored.　In　this　study,　two-dimensional　electronic　fingerprinting　was　demonstrated　and　integrated　into　a　memristive　true　random　number　generator　(TRNG).　For　the　device　function　of　the　TRNG,　two　modes　of　primitives　are　presented,　and　the　physical　entropy　sources　are　analyzed　via　a　recurrent　neural　network,　which　is　resilient　for　machine　learning　prediction.　For　anticounterfeiting　of　the　device,　a　two-dimensional　physical　unclonable　function　(PUF)　could　provide　a　high　entropy　value　and　multiple　verification　codes.　Because　of　its　extremely　high　surface-to-volume　ratio,　high　sensitivity　to　the　environment,　inevitable　randomness　introduced　in　the　fabrication　process,　and　the　ability　to　be　transferred　onto　arbitrary　substrates　(easy　to　integrate　into　a　single　device),　this　two-dimensional　PUF　device　could　be　a　general　solution　for　anticounterfeiting　of　nanoelectronics.