Lightweight machine learning framework for efficient DDoS attack detection in IoT networks

The rapid proliferation of Internet of Things (IoT) devices has introduced significant security challenges, with Distributed Denial of Service (DDoS) attacks posing a critical threat to network integrity. Traditional detection methods often rely on computationally intensive models, rendering them unsuitable for resource-constrained IoT environments. To address this limitation, this study proposes a lightweight and scalable machine learning-based DDoS detection framework specifically designed for IoT networks. Utilizing the NSL-KDD dataset, the framework employs an Extra Trees Classifier (ETC) for feature selection, reducing dimensionality while retaining critical attributes. Reduced features were selected to enhance performance and reduce processing cost. Three supervised learning models, Random Forest, Logistic Regression, and Naïve Bayes, were implemented and evaluated based on their detection accuracy, precision, recall, and F1-score. Experimental results demonstrate that the Random Forest model achieves exceptional accuracy (99.88%), precision (99.93%), recall (99.81%), and F1-score (99.87%), outperforming both Logistic Regression (91.61% accuracy) and Naïve Bayes (87.62% accuracy). Furthermore, the proposed framework significantly reduces computational overhead compared to deep learning-based approaches, making it highly suitable for IoT deployments. This research advances IoT security by providing a scalable, efficient, and accurate solution for detecting DDoS attacks, thereby bridging the gap between high-performance requirements and resource limitations in real-world IoT applications.