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Evaluating side-channel analysis (SCA) security is a complex process,involving applying several techniques whose success depends on humanengineering. Therefore, it is crucial to avoid a false sense of confidenceprovided by non-optimal (failing) attacks. Different alternatives have emergedlately trying to mitigate human dependency, among which deep learning (DL)attacks are the most studied today. DL promise to simplify the procedure bye.g. evading the need for point of interest selection or the capability ofbypassing noise and desynchronization, among other shortcuts. However,including DL in the equation comes at a price, since working with neuralnetworks is not straightforward in this context. Recently, an alternative hasappeared with the potential to mitigate this dependence without adding extracomplexity: Estimation of Distribution Algorithm-based SCA. In this paper, wecompare these two relevant methods, supporting our findings by experiments onvarious datasets.

Keep It Unbiased: A Comparison Between Estimation of Distribution Algorithms and Deep Learning for Human Interaction-Free Side-Channel Analysis