Researchers are exploring various machine learning approaches to improve the detection and classification of multiple attacks in IoT networks, with a focus on enhancing computational efficiency. Different methods are being evaluated, including lightweight machine learning, supervised learning, and hybrid deep learning approaches, using various datasets. The goal of these efforts is to develop effective and efficient intrusion detection systems for IoT networks, but the optimal approach remains to be determined.