@InProceedings{10.1007/978-3-031-21101-0_7,
author="Marchetti, Marco
and Ho, Edmond S. L.",
editor="Abd El-Latif, Ahmed A.
and Maleh, Yassine
and Mazurczyk, Wojciech
and ELAffendi, Mohammed
and I. Alkanhal, Mohamed",
title="Improving Deep Learning Model Robustness Against Adversarial Attack by Increasing the Network Capacity",
booktitle="Advances in Cybersecurity, Cybercrimes, and Smart Emerging Technologies",
year="2023",
publisher="Springer International Publishing",
address="Cham",
pages="85--96",
abstract="Nowadays, we are more and more reliant on Deep Learning (DL) models and thus it is essential to safeguard the security of these systems. This paper explores the security issues in Deep Learning and analyses, through the use of experiments, the way forward to build more resilient models. Experiments are conducted to identify the strengths and weaknesses of a new approach to improve the robustness of DL models against adversarial attacks. The results show improvements and new ideas that can be used as recommendations for researchers and practitioners to create increasingly better DL algorithms.",
isbn="978-3-031-21101-0"
}