Comprehensive Study: Machine Learning and Deep Learning Approaches in Intrusion Detection Systems - Azizi, Najoua; Jamali, Abdellah; Naja, Najib (Statistics, Optimization & Information Computing, 2026)
This paper presents a synthesis of approaches from various studies aimed at enhancing attack classification using machine learning (ML) and deep learning (DL) models. The works studied cover diverse aspects of cybersecurity, with a particular focus on intrusion detection systems (IDS) and Internet of Things (IoT) security. The paper provides an overview of the datasets used to train ML and DL models, the metrics used to evaluate the performance of these techniques, outlines the process for implementing them, and discusses perspectives and future research directions.