Federated Spatiotemporal Graph Learning for Passive Attack Detection in Smart Grids

ArXi:2510.02371v2 Announce Type: replace-cross 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 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.