Generative- AiEnabled Lightweight Traffic Detection Architecture for Programmable Gateways in Wireless Networks

The rapid growth of 5G and 6G networks has introduced complex traffic patterns and stringent real-time demands. Traditional SDN architectures struggle to meet the low-latency and dynamic requirements of wireless environments due to high communication overhead and rigid hardwares. Programmable switches, with their ability to dynamically cus-tomize data plane behavior, offer a more flexible solution for real-time traffic management at the network edge. However, most existing solutions rely on offline models with limited real-time detection capabilities, resulting in increased overhead and suboptimal performance. In this paper, we present Gendetect, a generative-AI enabled lightweight traffic detection architec-ture for programmable wireless gateways. Gendetect employs generative knowledge distillation to train decision tree-based models, enabling efficient online training and adaptive updates. By generating synthetic training data in real-time, it reduces the need for frequent control plane interactions, mitigating north-south overhead. Additionally, a feature selection mechanism optimizes resource utilization, balancing table entry consumption and detection accuracy. Extensive simulations demonstrate that Gendetect significantly improves traffic detection performance while reducing match-action table entries, making it well-suited for dynamic and resource-constrained wireless networks.