With the rapid development of the digital society, hardware security has become a pressing issue in information security. Although the Post-Quantum Cryptography (PQC) is known for its high security, it is still threatened by hardware Trojan attacks. In this paper, the Classic McEliece algorithm is applied to hardware implementation and a hardware Trojan detection method based on sensor chains for PQC Classic McEliece circuits is proposed. The deployment of the sensor chain consisting of 12 ring oscillators (ROs) on the PQC Classic McEliece circuits is precisely controlled by employing an incremental compilation technique. The validation experiments are performed on an Altera Cyclone-II EP2C35F672C8 FPGA development board, and the results show that the hardware Trojans at RO6 and RO12 are successfully detected using the normalized difference algorithm. The Support vector machine (SVM) algorithm achieves an accuracy of 72%, the logistic regression algorithm achieves an accuracy of 92%, and the K-means algorithm achieves an accuracy of 97%. These results strongly support the effectiveness and accuracy of the proposed hardware Trojan detection method based on sensor chains for the PQC Classic McEliece circuits, and also suggest that the method is applicable to the protection of other cryptographic circuits.
Convolutional neural networks (CNN) are computationally intensive algorithms with rich application scenarios. In some applications, convolutional neural networks need to be deployed in embedded devices close to sensors. Field Programing Gate Arrays (FPGA) is highly favored in the research of convolutional neural network accelerators due to its design flexibility and low power consumption. Therefore, this article designs a convolutional neural network VGG16 based on FPGA. To reduce the area of hardware layout and design complexity, the design verification of VGG16 is completed on the Xilinx Zynq XC7020 development board and solved the problem of insufficient internal resources.
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