Federated Spatiotemporal Graph Learning for Passive Attack Detection in Smart Grids

• URL: http://arxiv.org/abs/2510.02371v1
• Date: Mon, 29 Sep 2025 08:52:30 GMT
• Title: Federated Spatiotemporal Graph Learning for Passive Attack Detection in Smart Grids
• Abstract summary: This paper introduces a graph-centric, multimodal detector that fuses physical-layer and behavioral indicators over temporal windows to detect passive attacks.<n>The model achieves a testing accuracy of 98.32% per-timestep and 93.35% per-sequence at 0.15% FPR.
• Score: 2.721477719641864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Smart grids are exposed to passive eavesdropping, where attackers listen silently to communication links. Although no data is actively altered, such reconnaissance can reveal grid topology, consumption patterns, and operational behavior, creating a gateway to more severe targeted attacks. Detecting this threat is difficult because the signals it produces are faint, short-lived, and often disappear when traffic is examined by a single node or along a single timeline. This paper introduces a graph-centric, multimodal detector that fuses physical-layer and behavioral indicators over ego-centric star subgraphs and short temporal windows to detect passive attacks. To capture stealthy perturbations, a two-stage encoder is introduced: graph convolution aggregates spatial context across ego-centric star subgraphs, while a bidirectional GRU models short-term temporal dependencies. The encoder transforms heterogeneous features into a unified spatio-temporal representation suitable for classification. Training occurs in a federated learning setup under FedProx, improving robustness to heterogeneous local raw data and contributing to the trustworthiness of decentralized training; raw measurements remain on client devices. A synthetic, standards-informed dataset is generated to emulate heterogeneous HAN/NAN/WAN communications with wireless-only passive perturbations, event co-occurrence, and leak-safe splits. The model achieves a testing accuracy of 98.32% per-timestep (F1_{attack}=0.972) and 93.35% per-sequence at 0.15% FPR using a simple decision rule with run-length m=2 and threshold $\tau=0.55$. The results demonstrate that combining spatial and temporal context enables reliable detection of stealthy reconnaissance while maintaining low false-positive rates, making the approach suitable for non-IID federated smart-grid deployments.

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