Adaptive Chosen-Plaintext Deep-Learning-Based Side-Channel Analysis

Profiled side-channel analysis presents a significant risk to embedded devices in Internet of Things (IoT). Typically, a single trace is insufficient to successfully key recovery in practical scenarios. It still requires several traces based on Bayes' posterior probability. In this article, we introduce a chosen-plaintext (CP) strategy into the deep learning-based profiled attacks to improve the attack efficiency. First, we present a general strategy to profile the leakage model by exploiting the sensitivity analysis and clustering analysis. The leakage model derived from deep neural network is to characterize the leakage of the target algorithm. Second, we propose an adaptive CP method in the deep learning-based attack, transforming the conditional probability distribution of the leakage into the entropy of the key candidates under the profiled leakage model. Finally, we evaluate the efficiency of the attack by practical measurements. The results demonstrate that the proposed method requires fewer traces to retrieve the key of AES on devices of different types, e.g., Smartcard, FPGA, and ARM. Moreover, our attack improves the attack efficiency on masked implementations.