What I Published?
Publications
To Overfit, Or Not to Overfit: Improving the Performance of Deep Learning-based SCA
By: Azade Rezaeezade, Guilherme Perin, and Stjepan Picek
Usually, overfitting or poor generalization would be mitigated by adding more measurements to the profiling phase to reduce estimation errors. This paper provides a detailed analysis of different deep learning model behaviors and shows that adding more profiling traces as a single solution does not necessarily help improve generalization.
Regularizers to the Rescue: Fighting Overfitting in Deep Learning-based Side-channel Analysis
By: Azade Rezaeezade and Lejla Batina
Regularization techniques are popular solutions to overfitting. At the same time, the works in the side-channel domain show sporadic utilization of regularization techniques. What is more, no systematic study investigates these techniques' effectiveness. In this paper, we aim to investigate the regularization effectiveness by applying four powerful and easy-to-use regularization techniques.
