What question did this study set out to answer?

The main aim is to detect DDoS attacks in smart home settings using various machine learning models.

January 20, 2026Open Access

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

Key Points

The main aim is to detect DDoS attacks in smart home settings using various machine learning models.
Collected real smart home traffic data consisting of normal and attack activity for 11.5 hours.
Implemented and evaluated k-Nearest Neighbour (k-NN) and Artificial Neural Network (ANN) models.
Assessed model performance using self-collected data rather than benchmark datasets.
k-NN model achieved an accuracy of 97.13%.
ANN model achieved an accuracy of 81.7%.

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.

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Cite This Study

Raja et al. (Sat,) studied this question.

synapsesocial.com/papers/696ed06d6d8d470fca57ab5d https://doi.org/https://doi.org/10.1038/s41598-025-32004-y

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