Ensemble-based detection of distributed denial-of-service attacks in IoT networks using majority decision mechanisms.

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.