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Machine-Learning Anomaly Detection for Early Identification of DDOS in Smart Home IoT Devices

Summary:

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.

Author(s):

Roland Lamptey    
City St George’s, University of London
United Kingdom

Mohammad Saedi    
City St George’s, University of London
United Kingdom

Mohammad Saedi (M'19) received his BEng and MEng (Hons) in Computer Engineering from Iran and a PhD in Computer Science (5G Network Security) from Ulster University, UK. He has served as a Senior Lecturer in Computing at Sheffield Hallam University and is currently a Lecturer in the Department of Computer Science at City St George’s, University of London. Dr Saedi has also worked as a BTIIC researcher specializing in secure communication over 5G and has industry experience as a Network Security Specialist. His expertise covers 5G security, computer networks, data science, AI/ML, V2X, and IoT.

Vladimir Stankovic    
City St George’s, University of London
United Kingdom