Amplitude-modulated EM side-channel attack on provably secure masked AES

Abstract

AbstractRecently a new type of side channels was discovered, called amplitude-modulated electromagnetic (EM) emanations from mixed-signal circuits. Unlike power analysis or near field EM analysis, attacks based on amplitude-modulated EM emanations do not require the close physical access to the victim device, which makes the attack particularly threatening. However, all existing amplitude-modulated EM attacks on AES focus on implementations of unprotected TinyAES, which is less likely to be used when the implementation is not overly resource constrained. This paper presents the first deep learning based side-channel attack on AES-128 with a Rivain–Prouff masking scheme by using amplitude-modulated EM emanations as the side channel. Rivian–Prouff masking scheme is a provably secure higher-order masking scheme for AES. To bypass the theoretical strength of the addition-chain based Boolean masked SBox, we train neural networks on trace segments corresponding to the MixColumns operation in which the data loading instructions for SBox output leak information. By comparing two different training strategies, we show that it is feasible to recover the key from an ARM Cortex-M4 CPU implementation of AES-128 with a Rivain–Prouff masking scheme by using the amplitude-modulated EM emanations leaked from the victim device, which has a Bluetooth module embedded on the board.