GNN-enhanced Traffic Anomaly Detection for Next-Generation SDN-Enabled Consumer Electronics

HE rapid expansion of the Internet of Things (IoT) has seamlessly integrated consumer electronics (CE) devices--such as smartphones, smartwatches, and laptops--into our daily lives, enabling remote access and connectivity across diverse sectors like e-healthcare, smart cities, and intelligent transportation [1]. The CE market is projected to reach 2.873 billion users by 2025, driven by the capacity of nearly every device to generate and share data [2], [3]. CE networks, composed of heterogeneous devices from various manufacturers, present unique challenges due to large-scale deployment, high device diversity, and limited computational resources [1], [4]. Unlike traditional IT networks, CE devices such as smart home appliances and wearables require lightweight, secure, and low-latency communication [5]. Their traffic is often encrypted, intermittent, and follows irregular patterns, complicating the task of network anomaly detection (NAD) [6]. Security breaches in CE can have severe consequences, including privacy invasion, financial loss, and physical safety risks, and compromised devices can be conscripted into botnets for large-scale attacks like DDoS campaigns [7]-[9]. While existing machine learning (ML) and deep learning (DL) methods for NAD have shown promise, they often suffer from time-consuming feature extraction processes and require extensive manual configuration, making them ill-suited for the dynamic nature of CE networks [10]. To overcome these limitations, advanced architectures like Compute First Networking (CFN) and Software-Defined Networking (SDN) are gaining traction.