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In this letter, a novel secret merchant‐4 (SM4) cryptographic circuit implementation is proposed against higher order power analysis attacks (PAAs). Four different random number generators (RNGs) are embedded into the SM4 architecture for breaking the correlation between the processed data and monitored power dissipation against PAAs. Firstly, fake keys are created by the first RNG to scramble the critical information related with the actual secret key. Furthermore, the second RNG controls the implementations of substitution boxes (Sboxes) with composite fields or look‐up tables randomly while the third RNG randomizes the substitution locations with respect to these Sboxes. Ultimately, the fourth RNG randomly swaps the behaviors of the fake SM4 and true SM4 to further break the critical correlation. Under the assistance of the four embedded RNGs, the proposed SM4 cryptographic architecture is capable of resisting against fourth‐order PAAs effectively with a 300 Mbps throughput and 165,354 μm2 area after synthesizing in the TSMC 90 nm process design kits (PDK). [ABSTRACT FROM AUTHOR]

Based on the idea of software and hardware co-design, this article uses HLS tools to design and implement a convolutional neural network accelerator on the PYNQ-Z2 platform, and uses the matrix cutting optimization method for convolution operations to balance resource consumption and computing resources , so that the performance of the accelerator is optimized. This article uses the MNIST data set to test the performance of the accelerator IP core. The experimental results show that: for a single image test, the accelerator achieves an acceleration effect of 5.785 compared with the ARM platform, and an acceleration of 9.72 for a 1000 image test. As a result, as the number of test images continues to increase, the performance of the accelerator will become better and better.

Side-channel attacks (SCAs) are powerful noninvasive attacks that can be used for leaking the secret key of integrated circuits (ICs). Numerous countermeasures were proposed to elevate the security level of ICs against SCAs. Unfortunately, it is quite inconvenient to predict the security levels of these countermeasures since no solid mathematical model exists in the literature. In this paper, neural network (NN)-based entropy is proposed to model the resilience of a system against SCAs. The NN-based entropy model well links the side-channel leakages and probabilities with the neurons and weights of NNs, respectively. In such a circumstance, the NN-based entropy can be used for modeling the robustness of countermeasures since a one-to-one relationship is established between the NN-based entropy and the measurement-to-disclose (MTD) enhancement ratio related with the countermeasures. As demonstrated in the result, the proposed NN-based entropy metric shows 100% consistency with the MTD enhancement ratio if multiple SCA countermeasures are employed into a system. [ABSTRACT FROM AUTHOR]