An Enhanced ANN-RF Hybrid Algorithm for DoS Attack Detection in IoT Networks

Abstract

Denial-of-Service (DoS) attacks continue to threaten the availability and dependability of Internet of Things (IoT) networks. Because many IoT devices have limited processing power, memory, and built-in protection, they are frequently exploited to generate abnormal traffic that blocks legitimate communication. Signature-driven security tools are often unable to cope with changing attack behaviour. This paper presents a hybrid intrusion detection model that combines Artificial Neural Networks (ANNs) and Random Forest (RF) classification for DoS detection in IoT networks. The ANN component learns non-linear traffic representations, while the RF component performs the final ensemble-based classification. The model was implemented in MATLAB and evaluated using detection accuracy, false positive rate (FPR), and latency. The results show that the hybrid ANN-RF model reached 93% detection accuracy and 5% FPR, outperforming standalone ANN and RF models. The findings indicate that the proposed approach can support reliable and scalable real-time intrusion detection for IoT applications such as smart homes, healthcare systems, and industrial automation.