Machine Learning Techniques for Anomaly Detection in IoT and WSN: A review

Quick Internet of Things (IoT) and Wireless Sensor Networks (WSN) proliferation have considerably raised real-life and automation monitoring, data-based decision-making over various fields such as industrial systems, healthcare, and smart cities. Although great IoT device development defines security vulnerabilities and operational risks, it signifies strong anomaly diagnosis algorithms for recognizing system failures, unusual behaviors, and cyber threats. Traditional rule-based and statistical techniques cope with controlling active, massive, and high-dimensional IoT data aspects, creating methods of machine learning (ML) that are promising alternatives for appropriate and scalable unusual diagnosis. The present paper shows a general review of ML-based unusual diagnosis strategies in IoT and WSN, grouping them into hybrid, unsupervised, and supervised learning methods. In addition, it examines deep learning architectures, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Transformer-based models, highlighting their strengths in capturing complex spatial and temporal dependencies in sensor data. Despite their efficiency, ML-based techniques meet some issues like real-life limitations, data scarcity, high computational costs, adversarial vulnerabilities, and a shortage of generalization over various IoT areas. For considering such issues, this review describes the present paper's directions. Though state-of-the-art methods’ analysis and highlighting future trends, the present paper targets present worthy perspectives for investigators and practitioners in improving more adaptive, safe, effective ML-based unusual diagnosis responses for IoT and WSN