Benchmarking presence-only passive reconnaissance in smart-grid communications is challenging because the adversary is receive-only, yet nearby observers can still alter propagation through additional shadowing and multipath that reshapes channel coherence. Public smart-grid cybersecurity datasets largely target active protocol- or measurement-layer attacks and rarely provide propagation-driven observables with tiered topology context, which limits reproducible evaluation under strictly passive threat models. This paper introduces an IEEE-inspired, literature-anchored benchmark dataset generator for passive reconnaissance over a tiered Home Area Network (HAN), Neighborhood Area Network (NAN), and Wide Area Network (WAN) communication graph with heterogeneous wireless and wireline links. Node-level time series are produced through a physically consistent channel-to-metrics mapping where channel state information (CSI) is represented via measurement-realistic amplitude and phase proxies that drive inferred signal-to-noise ratio (SNR), packet error behavior, and delay dynamics. Passive attacks are modeled only as windowed excess attenuation and coherence degradation with increased channel innovation, so reliability and latency deviations emerge through the same causal mapping without labels or feature shortcuts. The release provides split-independent realizations with burn-in removal, strictly causal temporal descriptors, adjacency-weighted neighbor aggregates and deviation features, and federated-ready per-node train, validation, and test partitions with train-only normalization metadata. Baseline federated experiments highlight technology-dependent detectability and enable standardized benchmarking of graph-temporal and federated detectors for passive reconnaissance.

翻译：对智能电网通信中的仅存在性被动侦察进行基准测试具有挑战性，因为攻击者仅具备接收能力，但附近的观测者仍可通过额外的阴影和多径效应改变传播，从而重塑信道相干性。公开的智能电网网络安全数据集主要针对主动协议层或测量层攻击，很少提供具有分层拓扑背景的传播驱动观测数据，这限制了在严格被动威胁模型下的可重复评估。本文介绍了一种受IEEE启发、以文献为基础的基准数据集生成器，用于在分层家庭区域网络（HAN）、邻域区域网络（NAN）和广域网（WAN）通信图（包含异构无线和有线链路）上进行被动侦察。节点级时间序列通过物理一致的信道到度量映射生成，其中信道状态信息（CSI）通过测量现实的幅度和相位代理表示，这些代理驱动推断的信噪比（SNR）、数据包错误行为和延迟动态。被动攻击仅被建模为窗口化的额外衰减和相干性退化（伴随信道创新性增加），因此可靠性和延迟偏差通过相同的因果映射产生，无需标签或特征捷径。该发布提供了经过预热期去除的独立分割实现、严格因果的时间描述符、邻接加权的邻居聚合与偏差特征，以及支持联邦学习的按节点划分的训练、验证和测试分区（附带仅用于训练的归一化元数据）。基线联邦实验突出了技术相关的可检测性，并为被动侦察的图-时序检测器和联邦检测器提供了标准化基准测试框架。