Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Benchmarking Dataset for Presence-Only Passive Reconnaissance in Wireless Smart-Grid Communications

Bochra Al Agha, Razane Tajeddine

TL;DR

This paper introduces an IEEE-inspired, literature-anchored benchmark dataset generator for passive reconnaissance over a tiered Home Area Network, Neighborhood Area Network, and Wide Area Network communication graph with heterogeneous wireless and wireline links.

Abstract

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.

Benchmarking Dataset for Presence-Only Passive Reconnaissance in Wireless Smart-Grid Communications

TL;DR

This paper introduces an IEEE-inspired, literature-anchored benchmark dataset generator for passive reconnaissance over a tiered Home Area Network, Neighborhood Area Network, and Wide Area Network communication graph with heterogeneous wireless and wireline links.

Abstract

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.
Paper Structure (47 sections, 26 equations, 2 figures, 4 tables)

This paper contains 47 sections, 26 equations, 2 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Design Goals and Threat Model
  4. Design Goals
  5. Threat Model
  6. Topology and Feature Generation
  7. Communication Topology
  8. Topology Construction and Neighbor Modeling
  9. Feature Generation Pipeline
  10. Channel Modeling and Traffic Semantics
  11. Sampling semantics.
  12. Technology-Conditioned Surrogate Channel
  13. Latent fading and phase.
  14. Shadowing and interference.
  15. Measurement model.
  16. ...and 32 more sections

Figures (2)

  • Figure 1: Layered smart-grid communication topology (HAN/NAN/WAN) with device roles and communication technologies.
  • Figure 2: Normal vs. attack distribution shift on active samples (tx_count$>0$). KDE overlays are shown for CSI (dB), SNR, and log-latency, while packet error is shown via ECDF (bounded support). Presence-only perturbations (shadow-loss and coherence degradation) shift CSI/SNR downward and propagate to higher packet error and latency through the coherent channel-to-metrics chain. Multi-modality reflects heterogeneous technologies (ZigBee/Wi-Fi/LTE/LoRa/PLC).