Spatio-temporal graph ConvLSTM with hierarchical personalized federated learning for cooperative zero-day intrusion detection in 6G internet of vehicles

Engineering notes

In comparison to state-of-the-art federated baselines, experimental evaluations on real-world CAN datasets under realistic 6G-IoV settings show that the proposed framework achieves high detection performance for both known and zero-day attacks while significantly improving convergence speed and lowering system overhead.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

In growing 6G-enabled Internet of Vehicles (IoV) environments, in-car networks (IVNs) are more susceptible to sophisticated cyber threats, especially zero-day attacks that avoid signature-based detection. The centralised data dependency, lack of geographical awareness, and poor generalisation in heterogeneous and non-IID conditions are the limitations of current intrusion detection systems. This paper suggests a cooperative intrusion detection system that combines a spatio-temporal graph convolutional long short-term memory (ST-GConvLSTM) model with hierarchical personalised federated learning. The suggested method uses dynamic vehicle-to-vehicle graph structures to capture both intra-vehicle temporal patterns of CAN communications and inter-vehicle spatial dependencies. Scalable training is made possible by a three-tier learning architecture (vehicle, edge, and cloud) that protects data privacy and reduces concept drift. Furthermore, under limitations of latency, energy consumption, communication overhead, and privacy budget, a multi-objective reinforcement learning technique is used to dynamically optimise client involvement. In comparison to state-of-the-art federated baselines, experimental evaluations on real-world CAN datasets under realistic 6G-IoV settings show that the proposed framework achieves high detection performance for both known and zero-day attacks while significantly improving convergence speed and lowering system overhead. These findings demonstrate how well hierarchical federated optimisation and spatiotemporal graph learning can be combined to create safe and scalable vehicular networks.

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