A Deep Learning Approach to Side-Channel Analysis of Cryptographic Hardware

With increased growth of the Internet of Things (IoT) and physical exposure of devices to adversaries, a class of physical attacks called side-channel analysis (SCA) has emerged which compromises the security of systems. While security claims of cryptographic algorithms are based on the complexity of classical cryptanalysis attacks, they exclude information leakage by implementations on hardware platforms. Recent standardization processes require assessment of hardware security against SCA. In this dissertation, we study SCA based on deep learning techniques (DL-SCA) as a universal analysis toolbox for assessing the leakage of secret information by hardware implementations. We demonstrate that DL-SCA techniques provide a trade-off between the amount of prior knowledge of a hardware implementation and the amount of measurements required to identify the secret key. A DL-SCA based on supervised learning requires a training set, including information about the details of the hardware implementation, for a successful attack. Supervised learning has been widely used in power analysis (PA) to recover the secret key with a limited size of measurements. We demonstrate a similar trend in fault injection analysis (FIA) by introducing fault intensity map analysis with a neural network key distinguisher (FIMA-NN). We use dynamic timing simulations on an ASIC implementation of AES to develop a statistical model for biased fault injection. We employ the model to train a convolutional neural network (CNN) key distinguisher that achieves a superior efficiency, nearly $10times$, compared to classical FIA techniques. When a priori knowledge of the details of hardware implementations is limited, we propose DL-SCA techniques based on unsupervised learning, called SCAUL, to extract the secret information from measurements without requiring a training set. We further demonstrate the application of reinforcement learning by introducing the SCARL attack, to estimate a proper model for the leakage of secret data in a self-supervised approach. We demonstrate the success of SCAUL and SCARL attacks using power measurements from FPGA implementations of the AES and Ascon authenticated ciphers, respectively, to recover entire 128-bit secret keys without using any prior knowledge or training data.