Machine learning for the detection and identification of attacks in the Internet of Things (IoT) is a critical research area focused on enhancing the security and resilience of IoT networks. Research papers in this domain explore supervised, unsupervised, and semi-supervised machine learning techniques to identify anomalies, intrusions, malware, and other cyber threats across heterogeneous IoT devices and communication channels. Key contributions include feature selection and extraction methods for IoT data, real-time intrusion detection systems (IDS), lightweight algorithms suitable for resource-constrained devices, and hybrid models combining ML with traditional security frameworks. Recent studies also investigate federated learning, edge/fog-assisted analytics, and deep learning integration to improve detection accuracy while preserving data privacy. Challenges such as imbalanced datasets, high-dimensional data, scalability, and adversarial attacks are actively addressed. By leveraging AI-driven intelligence and adaptive learning mechanisms, research in machine learning-based attack detection aims to create robust, proactive, and secure IoT ecosystems.