Intelligent Machine Learning Approaches for IoT Network Threat Detection and Security Optimization

This chapter aims to enhance Internet of Things (IoT) network security through machine learning techniques for threat detection and prevention. The focus is on optimizing detection accuracy while minimizing computational overhead. The primary contribution lies in developing an optimized security framework that balances detection accuracy with computational efficiency, which is crucial for resource-constrained IoT environments. The research comprehensively evaluates multiple ML algorithms including Random Forest, Decision Tree, Naive Bayes, Gradient Boosting, and k-Nearest Neighbors for classifying attack types in IoT traffic. Performance metrics assessed include accuracy, precision, recall, and F1-score across all models. Additionally, the research integrates Snort for intrusion detection/prevention system implementation alongside anomaly detection and behavioral analysis techniques. The study quantifies resource utilization for each approach, providing valuable insights into computational efficiency for real-world IoT deployments. This work addresses the critical challenge of securing the expanding IoT ecosystems while considering their inherent resource limitations.