An Efficient Approach Using Lightweight Machine Learning Models for Detection of DDoS Attacks on IoT Devices

Advancements in the Internet of Things (IoT) have introduced significant security challenges, with Distributed Denial of Service (DDoS) attacks emerging as one of the most critical threats. These attacks involve botnets controlled by attackers that flood networks with malicious traffic, disrupting legitimate services. As the global DDoS landscape evolves, it has become ever more critical for IoT devices to identify and mitigate these threats rapidly. Machine learning has emerged as a promising method for DDoS detection; however, the field lacks a standardized and efficient framework designed for the resource-constrained nature of IoT devices. Existing solutions often depend on overly complex models, overlooking the chance to utilize lightweight and efficient techniques for real-world deployment. To address these limitations, this research proposes a novel framework that combines feature selection and lightweight machine learning models for efficient DDoS detection in IoT environments. The proposed framework integrates feature selection techniques to identify the most relevant features for enhancing detection efficiency. It influences lightweight machine learning models to ensure compatibility with IoT devices with limited computational resources. Specifically, this study evaluates three machine learning models, Random Forest, Logistic Regression, and Naive Bayes, for binary classification of DDoS attacks, using the NSL-KDD dataset for evaluation. The framework seamlessly integrates feature selection with lightweight models, improving performance, increased efficiency, and lower computational overhead. The results demonstrate that the proposed framework significantly outperforms existing methods, achieving 99.88% detection accuracy with the Random Forest model, 91.61% with Logistic Regression, and 87.62% with Naive Bayes. This research advances IoT security by integrating feature selection with lightweight machine learning, providing practical and effective solutions for wireless applications.