Products secured by embedded cryptographic mechanisms may be vulnerable to side-channel attacks. Such attacks are based on the observation of some physique quantities measured during the device activity, whose variation may provoke information leakage and lead to a security flaw.
Today, such attacks are improved, even in presence of specific countermeasures, by deep learning based methods.
The goal of this thesis is go get familiarity with semi-supervised and self-supervised Learning state-of-the-art and adapt promising methods to the context of the side-channel attacks, in order to improve performances of the attacks in very complex scenarios. A particular attention will be given to attacks against secure implementations of post-quantum cryptographic algorithms.