Intelligent Intrusion Detection System With Autonomous Optimal Traffic Steering for Aerial-Aided Edge Computing

Aerial-aided edge computing (AEC) promises to provide low-latency computing services as a critical component for future low-altitude intelligent transportation systems. However, the complex edge network environment poses security challenges, as traditional intrusion detection systems (IDSs) struggle to handle AEC’s large traffic volume and resource constraints. To address this, we propose a collaborative detection mechanism called Switch IDS. First, Switch IDS adopts a packet-level detection solution to meet real-time detection requirements. Next, Switch IDS is designed for resource-constrained nodes. By introducing the Mixture of Experts (MoE) structure into the traditional deep learning (DL) model, it enables seamless and scalable multinode deployment. Switch IDS establishes a resource status-based capability for each Expert and incorporates steering loss in the loss function to enable autonomous near-optimal traffic steering, ultimately maximizing system processing capacity. Finally, the Switch IDS utilizes parallelized service function chaining (SFC) for practical multinode deployment. To the best of our knowledge, this is the first realization of multinode deployment and autonomous traffic steering for DL-based IDS. Experiments on public datasets show that Switch IDS and its multinode deployment scheme notably boost processing capacity while maintaining high detection performance.