Machine Learning Techniques for Anomaly Detection in Smart IoT Sensor Networks

Abstract

Detecting anomalies in Smart IoT Sensor Networks (SISNs) is crucial for identifying unusual events or behaviors that could indicate security breaches or operational issues. Traditional methods based on predefined rules often fall short due to the complex and dynamic nature of IoT environments. Machine learning (ML) techniques have emerged as effective alternatives, using algorithms that learn from data to automatically detect anomalies. This review article examines various ML approaches used for anomaly detection in SISNs, including supervised, unsupervised, and semi-supervised learning methods. It discusses essential aspects such as data preparation, feature engineering, and selecting suitable algorithms to improve detection accuracy and efficiency. Case studies illustrate how ML techniques are applied in practical IoT settings, demonstrating their effectiveness in detecting a range of anomalies. The article also explores evaluation metrics for assessing detection performance, focusing on metrics like precision, recall, and F1-score. Finally, the conclusion provides insights into current challenges, future research directions, and the potential impact of ML-based anomaly detection in enhancing the security and reliability of Smart IoT Sensor Networks.