Enhanced Detection of IoT-Based DoS Attacks Using A Hybrid ANN-RF Classification Model

Abstract

Denial of Service (DoS) attacks pose a significant threat to the integrity and availability of Internet of Things (IoT) networks, where interconnected devices are increasingly targeted due to their vulnerabilities. These attacks overwhelm systems with excessive traffic, disrupting legitimate services and potentially compromising sensitive data. Traditional detection methods often rely on predefined signatures, which struggle to keep pace with the evolving tactics employed by attackers. This study introduces a novel hybrid detection algorithm that integrates Artificial Neural Networks (ANN) and Random Forest (RF) classifiers, termed ANN-RF, to enhance the detection of DoS attacks in IoT environments. The ANN-RF model was evaluated based on critical performance metrics, including detection accuracy, False Positive Rate (FPR), and latency. Experimental results obtained through MATLAB demonstrate that the ANN-RF model achieves a detection accuracy of 93% and a low FPR of 5% when detecting 30 attacks, significantly outperforming standalone ANN and RF models, which recorded accuracies of 82% and 87%, and FPRs of 15% and 10%, respectively. Additionally, the ANN-RF model consistently maintains high detection accuracy, reducing false alarms and enhancing reliability as the number of attacks increases. Thus, the proposed ANN-RF model has strong potential to enhance real-time security in IoT networks by offering a scalable, accurate, and adaptive solution for DoS attack detection, with practical applications across domains such as smart homes, healthcare, and industrial control systems.