Identification and detection of DDoS attack on smart home infrastructure using machine learning models.

Journal: Scientific reports
Published Date: Jan 17, 2026

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.

Authors

  • Thejavathy Vengappa Raja
    Department of Computer Science, School of Science and Technology, Clifton Campus, Nottingham Trent University, Nottingham, NG11 8NS, UK.
  • Zoher Ezziane
    Department of Computer Science, School of Science and Technology, Clifton Campus, Nottingham Trent University, Nottingham, NG11 8NS, UK. [email protected].
  • Jun He
    Institute of Animal Nutrition, Sichuan Agricultural University, Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Key Laboratory of Animal Disease-resistant Nutrition and Feed of China Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Disease-resistant Nutrition of Sichuan Province, Ya'an, 625014, China.
  • Xiaoqi Ma
    School of Information Management, Heilongjiang University, Harbin, 150008, Heilongjiang, People's Republic of China.
  • Asmau Wali-Zubair Kazaure
    Department of Computer Science, School of Science and Technology, Clifton Campus, Nottingham Trent University, Nottingham, NG11 8NS, UK.

Keywords

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