Side -channel analysis (SCA) is a cryptanalytic technique that exploits physical leakages, such as power consumption or electromagnetic emanations, from cryptographic devices to extract secret keys used in cryptographic algorithms. Recent studies have shown that training SCA models with semi -supervised learning can effectively overcome the problem of few labeled power traces. However, the process of training SCA models using semi -supervised learning generates many pseudo -labels. The performance of the SCA model can be reduced by some of these pseudo -labels. To solve this issue, we propose ...
