Attack path analysis
Identities, permissions and systems are modelled as a graph to find paths from exposed entry points to critical assets, and the links whose removal cuts most paths.
GCRI · Cybersecurity and Digital Resilience
Network science for cybersecurity
Network science represents systems, dependencies and actors as graphs. In this field it maps attack paths through networks, concentration of digital dependencies, and the coordination patterns that reveal information operations.
01Register
Network science for cybersecurity on the record.
Method recordSTEM method
- MethodNetwork science and contagion
- Applied inCybersecurity and Digital Resilience
- Data3 inputs
- Checks3 validation steps
- Canonical pageSTEM Commons
02Application
What network science and contagion does here.
Network science and contagion study how the structure of connections among entities shapes the spread of failures, information and disease. It provides tools to analyse interdependent infrastructure, supply chains and financial systems. In cyber and digital resilience and critical infrastructure, network science identifies critical nodes and pathways through which shocks propagate across sectors. It informs targeted protection and diversification to limit cascades. The canonical description is on the STEM Commons.
Digital dependency concentration
Dependencies of services on providers, libraries and infrastructure are mapped to find nodes on which many services depend.
Coordination detection
Accounts that post the same content at the same times are linked in graphs to identify coordinated networks, with thresholds and false positive checks stated.
03Geography
Connected activity in Europe.
Night lights, population density and true-colour imagery over Europe, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Connected activity, for this page
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Connected activity, framed on Europe.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA HLS S30Sources and licences
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Observed (EO): NASA HLS S30 (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
- Optional layers (5 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Enhanced vegetation index, rolling 8 days: MODIS Terra EVI, NASA LANCE · 13 Oct 2026
04Data
What the method consumes.
Datasets and records used by network science and contagion on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Identity and permission data | Attack graphs | OBSERVED |
| Software dependency graphs | Concentration | OBSERVED |
| Platform data available to researchers | Coordination patterns | OBSERVED |
05Monitor
Network analysis.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Network analysis
Cybersecurity · Network science for cybersecurity · Run R-1892
Single point of failureGCRI · Cybersecurity and Digital Resilience
Dependency graph
nodes: assets · edges: dependencies
Most central nodes
| N3 | 0.34 | 6 | Critical |
| N4 | 0.14 | 7 | Normal |
| N5 | 0.11 | 6 | Normal |
| N6 | 0.41 | 6 | Normal |
Cascade size under removal
06Validation
How results are checked.
Models are checked by reproducing observed cascades and outbreaks. Robustness of results to missing or uncertain links is tested.
| Check | How it is done |
|---|---|
| Path validation | Attack paths are confirmed with controlled testing where permitted |
| Threshold sensitivity | Coordination detection is tested across thresholds |
| Ground truth comparison | Detected networks are compared with platform takedowns where available |
Network data are often incomplete or confidential. Static network models may miss adaptive behaviour during cascades.
07Simulator
A cascade through interdependent networks.
A hazard footprint takes out co-located assets in every layer; fragmentation, dependency between layers and overload of the survivors then spread the failure. The 3D view separates the layers by depth. Teaching model with stated illustrative values, not a prediction. The map on this page shows Europe; the model itself is not tied to a place.
Controls
- Assets per layer
- 18
- Hazard footprint
- 8% of area
- Assets that depend on another layer
- 60%
- Spare capacity (tolerance)
- 30%
Readouts
- Assets hit by the hazard
- 9%
- Assets still working
- 22%
- Amplification of the initial damage
- 8.4 times
- Steps to settle
- 3
Numbers, methods and sources
| Step | Data centres working (%) | Networks working (%) | Power working (%) |
|---|---|---|---|
| 0 | 94 | 83 | 94 |
| 1 | 39 | 50 | 78 |
| 2 | 28 | 17 | 28 |
| 3 | 28 | 11 | 28 |
Methods
- Interdependent networks Buldyrev and others (2010), Catastrophic cascade of failures in interdependent networks, Nature 464
- Overload cascades Motter and Lai (2002), Physical Review E 66
- Betweenness load Brandes (2001), Journal of Mathematical Sociology 25
- Multilayer networks Kivela and others (2014), Journal of Complex Networks 2
- Infrastructure interdependencies Rinaldi, Peerenboom and Kelly (2001), IEEE Control Systems 21(6)
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page's map can draw:
- Major roads Natural Earth
- Railways Natural Earth
- Seaports Natural Earth
- Airports Natural Earth
- Urban extents Natural Earth
- Night lights tonight, outages (VIIRS Day/Night Band) NASA/NOAA VIIRS (Suomi NPP) · register nasa.gibs.context-live-layers
- Natural events (NASA EONET) NASA EONET · register nasa.eonet.events
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Cybersecurity and Digital Resilience in Foundry
- Tested at Cybersecurity and Digital Resilience in Universe
- Codified in Digital, Cyber and AI in Standards
- Published in Cybersecurity and Digital Resilience in Reports
- Taught in Faculty of Technology and Digital Systems in Academy
08Connections
Topics that rely on this method.
09Twin and cascade simulator
Model network science for cybersecurity as a system. Run the cascade.
Assembled from the Nexus Standards area profile Cyber and digital resilience law alignment: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Dam failure
- Sea-ice loss
- Coastal erosion (set off downstream)
- River flood (set off downstream)
- Flash flood (set off downstream)
- Mass displacement (set off downstream)
- Biodiversity loss (set off downstream)
- Widespread power outage (set off downstream)
- Water supply shortfall (set off downstream)
- Arctic winter sea-ice collapse (set off downstream)
- Polar vortex disruption (set off downstream)
Systems and couplings
Controls
NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-9Contextual UNIs (deployments, instances, programme instances)NRS-X6Cyber and digital security riskNRS-H93Hazard family: Identity, trust and cryptographic failure (NHS-1 cyber.identity)NRS-K31Domain pack profile: Cyber national resilience (DOMAIN-031)NRS-H54Hazard family: Signal and platform disruption (NHS-1 cyber.platform)NRS-H86Hazard family: Telecom and data-centre failure (NHS-1 infra.digital)NRS-H94Hazard family: Systemic cyber events (NHS-1 cyber.systemic-cyber)
Observation
- NIST National Vulnerability Database APIs (listed for reference)
- CISA Known Exploited Vulnerabilities catalogue (listed for reference)
- EUR-Lex Cellar (EU legal acts and delegated acts) (listed for reference)
- Cybersecurity Supply Chain Risk Management
- Legal measures pillar of the Global Cybersecurity Index
Example geography
- North America: 243 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
- Vegetation disturbance alerts, 30 m (OPERA DIST-ALERT), NASA GIBS
10Stewardship
Who maintains the method.
Systems Science and Modelling stewards network science and contagion for the Consortium and reviews how it is applied across platforms.
11Methods and evidence
How network science for cybersecurity is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
System model
The couplings between the systems in network science for cybersecurity: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
12Connected
Where this page leads across the Consortium.
13Questions
Questions and answers.
What are the limits of network science and contagion in this field?
Network data are often incomplete or confidential. Static network models may miss adaptive behaviour during cascades.
How are results checked?
Models are checked by reproducing observed cascades and outbreaks. Robustness of results to missing or uncertain links is tested.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method's use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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GCRICybersecurity and Digital ResilienceStrategic SystemsScenario and Stress-Test
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