Optimizing Intrusion Detection for IoT: A Systematic Review of Machine Learning and Deep Learning Approaches With Feature Selection and Data Balancing

As the Internet of Things (IoT) continues expanding its footprint across various sectors, robust security systems to mitigate associated risks are more critical than ever. Intrusion Detection Systems (IDS) are fundamental in safeguarding IoT infrastructures against malicious activities. This systematic review aims to guide future research by addressing six pivotal research questions that underscore the development of advanced IDS tailored for IoT environments. Specifically, the review concentrates on applying machine learning (ML) and deep learning (DL) technologies to enhance IDS capabilities. It explores various feature selection methodologies aimed at developing lightweight IDS solutions that are both effective and efficient for IoT scenarios. Additionally, the review assesses different datasets and balancing techniques, which are crucial for training IDS models to perform accurately and reliably. Through a comprehensive analysis of existing literature, this review highlights significant trends, identifies current research gaps, and suggests future studies to optimize IDS frameworks for the ever‐evolving IoT landscape.