City Research Online

Abstract

The rapid adoption of Internet of Things (IoT) devices in smart homes has introduced security vulnerabilities, with Distributed Denial of Service (DDoS) emerging as a critical threat. Exploiting the often-unsecured nature of these interconnected devices, such attacks overwhelm network resources, causing severe disruptions and privacy breaches. We present a novel anomaly detection system for early-stage DDoS attack identification in smart home IoT environments. Using NS-3 simulator, a realistic IoT network dataset was generated, capturing normal and malicious traffic. Key traffic features, e.g., packet size and inter-arrival times, were extracted to train two lightweight Machine Learning (ML) models: One-Class Support Vector Machine (OCSVM) and Isolation Forest (IF). OCSVM model achieved superior performance with accuracies from 96% to 99% for various attacks, while the IF model performed marginally worse. We offer a lightweight and scalable solution for real-time deployment in resource-constrained IoT environments, a significant step to enhance smart home security.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	Copyright: IEEE, 2025.
Publisher Keywords:	Anomaly detection, DDoS attacks, smart home security, Internet of Things (IoT), machine learning, NS-3 simulator
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology School of Science & Technology > Computer Science
SWORD Depositor:	Symplectic Administrator

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