A Survey on Hybrid-CNN and LLMs for Intrusion Detection Systems: Recent IoT Datasets - Saida Elouardi; Anas Motii; Mohammed Jouhari; Abdoul Nasser Hassane Amadou; Mustapha Hedabou (IEEE Access)
Recently, the growing popularity of the Internet of Things (IoT) presents a promising opportunity not only for the expansion of various home automation systems but also for diverse industrial applications. By leveraging these benefits, automation is being implemented in industries, leading to the Industrial Internet of Things (IIoT). Although IoT simplifies daily activities that benefit human operations, it poses significant security challenges that warrant attention. Consequently, implementing an Intrusion Detection System (IDS) is a vital and effective solution. IDS aims to address the security and privacy challenges by detecting various IoT attacks. Various IDS methodologies, including those using Machine Learning (ML), Deep Learning (DL) and Large Language Models (LLMs), are employed to identify intrusions within the data; however, improvements to the detection systems are still needed. A literature survey on IDS in the IoT domain is provided, focusing primarily on the recent approaches used in the field. The survey aims to evaluate the literature, identify current trends, retest these approaches on recent data, and highlight open problems and future directions.