Far Field EM Side-Channel Attack on AES Using Deep Learning
Ruize Wang, Huanyu Wang, Elena Dubrova
Abstract
We present the first deep learning-based side-channel attack on AES-128 using far field electromagnetic emissions as a side channel. Our neural networks are trained on traces captured from five different Bluetooth devices at five different distances to target and tested on four other Bluetooth devices. We can recover the key from less than 10K traces captured in an office environment at 15 m distance to target even if the measurement for each encryption is taken only once. Previous template attacks required multiple repetitions of the same encryption. For the case of 1K repetitions, we need less than 400 traces on average at 15 m distance to target. This improves the template attack presented at CHES'2020 which requires 5K traces and key enumeration up to 223.