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<p>Traditional cryptographic algorithms often lack the efficiency required for IoT devices due to their heterogeneous and resource-constrained nature. However, the unclonability, resource efficient, and easy-to-implement properties of physically unclonable functions (PUFs) have attracted significant attention in IoT security, particularly for device authentication. Nonetheless, PUFs are vulnerable to modeling attacks, which exploit correlations within challenge response pairs (CRPs) to compromise security. One mitigation strategy is to employ random challenges, obscuring CRP correlations from adversaries. To address this, a novel lightweight true random number generator (TRNG) is proposed to generate challenges within CRP datasets. The resource efficient solution is implemented on Xilinx Zynq UltraScale+ MPSoC, Zynq 7000 series SoC chip (Zedboard), and Zybo boards. Experimental results demonstrate low power consumption (8%, 1%, and 1% for 128-bit, 64-bit, and 32-bit designs, respectively) and minimal resource utilization values are 4.3%, 2.9%, and 1.3% for 32-bit, 64-bit, and 128-bit implementations, rendering the TRNG suitable for IoT applications. Changes in PUF properties resulting from the application of the TRNG are analyzed, and NIST randomness and other statistical test results for the TRNG circuit are presented.</p>

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FPGA-based true random number generation and its application

  • 期刊论文

  • 发表日期：

    2026年3月25日

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  • Aditi Roy,
  • J. Kokila,
  • B. Shameedha Begum,
  • N. Ramasubramanian

摘要

Traditional cryptographic algorithms often lack the efficiency required for IoT devices due to their heterogeneous and resource-constrained nature. However, the unclonability, resource efficient, and easy-to-implement properties of physically unclonable functions (PUFs) have attracted significant attention in IoT security, particularly for device authentication. Nonetheless, PUFs are vulnerable to modeling attacks, which exploit correlations within challenge response pairs (CRPs) to compromise security. One mitigation strategy is to employ random challenges, obscuring CRP correlations from adversaries. To address this, a novel lightweight true random number generator (TRNG) is proposed to generate challenges within CRP datasets. The resource efficient solution is implemented on Xilinx Zynq UltraScale+ MPSoC, Zynq 7000 series SoC chip (Zedboard), and Zybo boards. Experimental results demonstrate low power consumption (8%, 1%, and 1% for 128-bit, 64-bit, and 32-bit designs, respectively) and minimal resource utilization values are 4.3%, 2.9%, and 1.3% for 32-bit, 64-bit, and 128-bit implementations, rendering the TRNG suitable for IoT applications. Changes in PUF properties resulting from the application of the TRNG are analyzed, and NIST randomness and other statistical test results for the TRNG circuit are presented.