Scoring the predictions: a way to improve profiling side-channel attacks

Side-channel analysis is an important part of the security evaluations of hardware components and more specifically of those that include cryptographic algorithms. Profiling attacks are among the most powerful attacks as they assume the attacker has access to a clone device of the one under attack. Using the clone device allows the attacker to make a profile of physical leakages linked to the execution of algorithms. This work focuses on the characteristics of this profile and the information that can be extracted from its application to the attack traces. More specifically, looking at unsuccessful attacks, it shows that by carefully ordering the attack traces used and limiting their number, better results can be achieved with the same profile. Using this method allows us to consider the classical attack method, i.e., where the traces are randomly ordered, as the worst-case scenario. The best-case scenario is when the attacker is able to successfully order and select the best attack traces. A method for identifying efficient ordering when using deep learning models as profiles is also provided. A new loss function “scoring loss” is dedicated to training machine learning models that give a score to the attack prediction and the score can be used to order the predictions.