  Numerous previous works have studied deep learning algorithms applied in the
context of side-channel attacks, which demonstrated the ability to perform
successful key recoveries. These studies show that modern cryptographic devices
are increasingly threatened by side-channel attacks with the help of deep
learning. However, the existing countermeasures are designed to resist
classical side-channel attacks, and cannot protect cryptographic devices from
deep learning based side-channel attacks. Thus, there arises a strong need for
countermeasures against deep learning based side-channel attacks. Although deep
learning has the high potential in solving complex problems, it is vulnerable
to adversarial attacks in the form of subtle perturbations to inputs that lead
a model to predict incorrectly.
  In this paper, we propose a kind of novel countermeasures based on
adversarial attacks that is specifically designed against deep learning based
side-channel attacks. We estimate several models commonly used in deep learning
based side-channel attacks to evaluate the proposed countermeasures. It shows
that our approach can effectively protect cryptographic devices from deep
learning based side-channel attacks in practice. In addition, our experiments
show that the new countermeasures can also resist classical side-channel
attacks.
