Identification and detection of DDoS attack on smart home infrastructure using machine learning models
Abstract
This study investigates Distributed Denial-of-Service (DDoS) attack detection within smart home environments using both traditional machine learning and deep learning approaches. Real smart home traffic data, collected approximately 11.5 h of normal and attack activity, was used to implement and evaluate two models: k-Nearest Neighbour (k-NN) and an Artificial Neural Network (ANN). The k-NN model achieved an accuracy of 97.13%, while the ANN achieved 81.7% accuracy under the same dataset conditions. Unlike previous studies relying solely on benchmark datasets, this work uses self-collected smart home data to assess model feasibility and real-world deployment potential.
Fields of Study & Subjects
Network Security and Intrusion Detection
Internet Traffic Analysis and Secure E-voting
Adversarial Robustness in Machine Learning
Publication details
- Journal / source
- Scientific Reports
- Volume
- 16
- Issue
- 1
- Pages
- 2238–2238
- Publication date
- 17 January 2026
- Language
- EN