Being Patient and Persistent: Optimizing An Early Stopping Strategy for Deep Learning in Profiled Attacks
Year: 2023
Type: article
Abstract: The absence of an algorithm that effectively monitors the deep learning models used in side-channel attacks increases the difficulty of a security evaluation. If an attack is unsuccessful, that could be due to multiple reasons. It can be that we are indeed dealing with a resistant implementation, but it is possible that the deep learning model used is faulty. In this contribution, we formalize two conditions,persistenceandpatience, for a deep learning model to be optimal and we propose an early stopping algorithm that reliably recognizes the model's optimal state during training. The novelty of our solution is in an efficient implementation of guessing entropy estimation as a success metric used to measure the strength of a side-channel adversary. As a result, the model which uses our strategy for learning converges with fewer traces than other known methods.
Source: IEEE Transactions on Computers
Institutions: Radboud University Nijmegen, Ikerlan
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3216624979