Ensemble-based detection of distributed denial-of-service attacks in IoT networks using majority decision mechanisms
Year: 2026
Type: article
Abstract: The widespread adoption of Internet of Things (IoT) devices has increased security risks, especially from Distributed Denial of Service (DDoS) attacks that exploit their limited resources. Existing Intrusion Detection Systems (IDS) often fail to provide high accuracy and produce excessive false positives when dealing with diverse traffic patterns in IoT networks. This study introduces a Majority Voting (MV) ensemble approach that combines five high-performance Machine Learning algorithms (ML) to improve DDoS attack detection. Using advanced preprocessing methods, such as hybrid sampling and information-augmented feature selection, the proposed framework achieves detection accuracies of 99.87% to 100% for DNS, NetBIOS, LDAP, UDP, and SNMP attacks on the CICDDOS2019 dataset. Unlike single-algorithm models, the MV approach significantly reduces the false positive rate while remaining computationally efficient, making it ideal for resource-constrained IoT environments. Thus, this research presents a scalable and robust IDS solution that enhances security for critical applications in smart cities, healthcare, and industrial systems. By integrating diverse classifiers, this method overcomes the limitations of individual models and ensures reliable detection of evolving DDoS threats in heterogeneous IoT networks.
Source: Scientific Reports
Authors: Suha Cheng, Xu Feng
Institution: Henan University of Technology
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7141769242