Findings demonstrated that such approaches may prevent an adversary from acquiring hidden IDs or asymmetric encryption via infiltrative, learning, side channel, and computer assaults. Radio-Frequency Systems (RFS) identification architectures provide safe data transmission within the web. In contrast extreme, Unaccessible Physical Function (UPF) might exploit manufacturing process irregularities to automatically identify microchips, making a UPF-based system incredibly durable and secure at a reasonable cost. They offer RFS-UPF, a Deep Neural Network (DNN) based methodology that level processes wireless node authentication utilizing the effects of intrinsic variability on RFS features of the remote controls (Tx), identified through in-situ supervised learning at the wireless sensors (Tx). A proposed method makes use of the current asymmetric RFS communication infrastructure by accumulating any special transistors to semantic segmentation or UPF invention. Similar to a human listener's brain works, Rx assumes a full burden of device identification at the gateway. According to experimental results, which include process performance at a predefined 65 nm threshold voltage and characteristics like LO misalignment and I-Q differences found using a predictive model with 52 hidden nodes, a framework could distinguish up to 4800 transmitters with the durability of 99.9% under different channel quality, without the necessity of conventional preambles.