A Comparative Analysis of Machine Learning Techniques for Anomaly Detection in IoT Networks Using Secondary Data

The rapid expansion of Internet of Things (IoT) devices has introduced significant security challenges due to their limited computational capabilities and increased exposure to cyber threats. Detecting anomalies in IoT networks has become essential for maintaining system integrity and preventing unauthorized activities. This paper presents a comparative analysis of various machine learning techniques for anomaly detection using secondary data derived from existing research studies. Techniques such as Random Forest, Decision Tree, Naïve Bayes, Support Vector Machine (SVM), and Neural Networks are evaluated based on performance, computational complexity, and suitability for resource-constrained environments. The analysis highlights that lightweight models provide a practical balance between efficiency and detection accuracy, whereas deep learning techniques offer improved performance at the cost of higher resource consumption. The study also emphasizes the importance of selecting appropriate models based on system requirements and available computational resources.