Side channels are electronic device interfaces that can unintentionally reveal information about the device's state or processing. They have become a source of vulnerabilities in cryptographic algorithms, leading to side-channel attacks that can compromise sensitive information. Deep learning models have emerged as effective tools for side-channel evaluation due to their ability to interpret side-channel information. However, designing an efficient deep learning network for side-channel attack evaluation requires consideration of various factors, such as network architecture and hyperparameters selection. Furthermore, a single deep learning model cannot evaluate multiple cryptographic implementations, which limits its practicality.
To address these challenges, this thesis proposes new processes and methodologies to improve the training process of deep learning models and modify their architecture for side-channel analysis evaluation. The research focuses on integrating Six Sigma methodology, early stopping framework, feature reduction, and transfer learning approaches to enhance the training process. Additionally, new ways to design the architecture of the deep learning model are proposed. The aim is to improve the efficiency of side-channel evaluation by designing deep learning models that can evaluate multiple cryptographic implementations.
Servio Paguada is a Ph.D. student at Radboud University in Nijmegen, the Netherlands, and is also part of the Ph.D. students' group at IKERLAN Technology Research Centre in Arrasate-Mondragón, Gipuzkoa, Spain. He earned a B.Eng. in System Engineering from Universidad Nacional Autonoma de Honduras in 2011, and in the same year, he also obtained a B.Sc. in Mathematics with Informatic Applications from the same university. He holds an M.Sc. degree in Embedded Systems from Mondragon University, Basque Country, Spain, in 2016, and a master's degree in Information Technology Management from Universidad Tecnologica Centroamericana in 2014. Servio has been a lecturer at Universidad Nacional Autonoma de Honduras from 2012-2017, and from 2017 to 2019, he became the main lecturer. Currently, he works at IKERLAN's Industrial Cybersecurity team as a researcher.