Adversarial Attacks in IoT: A Performance Assessment of ML and DL Models

The rise of IoT devices has led to significant advancements but also new security challenges. This paper assesses the performance of various machine learning (ML) models—Decision Trees, Naïve Bayes, Support Vector Machines (SVMs), and a deep learning model (CNN)—against adversarial attacks using the IoT-23 dataset. Attacks tested include the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD). Findings show that Decision Trees are the most robust, while CNNs are the most vulnerable, highlighting the need for improved defenses in IoT systems and suggesting new research avenues in adversarial learning.