Machine Learning Algorithms for Anomaly Detection in IoT Networks

Abstract

Anomaly detection in IoT networks is a pivotal task for identifying unexpected events that can yield crucial insights in sectors like healthcare, finance, and security. This paper explores various machine learning algorithms for anomaly detection, including supervised, unsupervised, and semi-supervised approaches. It discusses the challenges in implementing these algorithms in IoT environments, emphasizing the need for lightweight and efficient solutions. Preprocessing techniques, evaluation metrics, and case studies are examined, providing a comprehensive overview of practical applications and performance evaluation methods. The paper presents a case study on anomaly detection in an IoT-based temperature monitoring system using a Gaussian Mixture Model (GMM). The study demonstrates the successful integration of the algorithm, emphasizing benefits such as preventive maintenance, quality assurance, and operational efficiency. Performance metrics such as precision, recall, and F1 score are utilized for evaluation, showcasing the algorithm's effectiveness in identifying anomalies. Future research directions are outlined, emphasizing the need for real-time anomaly detection with limited resources, incorporating human expertise in decision-making, and addressing ethical considerations. The importance of diversity and transparency in algorithm development is highlighted, and suggestions for integration of additional sensors, dynamic threshold adjustments, and optimized edge computing are proposed. In conclusion, the paper emphasizes the significance of machine learning algorithms in anomaly detection for IoT networks, offering insights into their applications, challenges, and future directions. It underscores the need for continuous exploration and adaptation to evolving challenges, ensuring the security, effectiveness, and ethical considerations in the development and deployment of anomaly detection algorithms in IoT networks.