Correlated Slashing Risk Analyzer for Geographically Distributed Node Clusters

[JamesEjembi]

Problem Statement / Feature Objective

Operators running validators across multiple data centers risk correlated slashing events when a shared infrastructure dependency (same cloud provider, same ISP, same power grid) causes simultaneous failures. Build a risk analyzer that takes a cluster topology map (node coordinates + provider metadata), cross-references it against known slashing vectors (same IP range, same ASN, same cloud region), and computes a correlated slashing risk score for each node cluster with mitigation recommendations.

Technical Invariants & Bounds

• Risk score per cluster = 0.4 × (shared_IP_count / total_nodes) + 0.3 × (shared_ASN_count / total_nodes) + 0.3 × (shared_cloud_region_count / total_nodes)
• Cluster detected via DBSCAN density-based clustering on geocoordinates (ε=500km, minPts=3)
• Infrastructure metadata sourced from: (1) node self-reported config, (2) IP geolocation API, (3) BGP ASN lookup
• Risk tier thresholds: Low (<0.25), Medium (0.25–0.5), High (0.5–0.75), Critical (>0.75)
• Mitigation recommendations: for each risk factor, suggest diversification strategies (e.g., "Move 3 nodes to different ASN 12345")
• Cluster map must render up to 500 nodes on a Canvas global map projection (equirectangular) with cluster overlays

Codebase Navigation Guide

• src/hooks/useCorrelationRisk.ts – risk analysis hook
• src/workers/riskClusterWorker.ts – web worker for DBSCAN clustering
• src/components/canvas/RiskTopologyMap.tsx – canvas map renderer
• src/services/infrastructureService.ts – IP/ASN/cloud metadata service
• src/utils/dbscan.ts – DBSCAN clustering implementation
• src/store/riskSlice.ts – state management for risk data

Implementation Blueprint

1. Implement src/utils/dbscan.ts – DBSCAN clustering algorithm with ε=500km, minPts=3 using Haversine distance calculation on lat/lng coordinates
2. Build src/services/infrastructureService.ts – aggregates node infrastructure metadata: IP (from node config), ASN (via BGP lookup API), cloud region (from node labels/tags)
3. Create src/workers/riskClusterWorker.ts – receives node list → runs DBSCAN → returns cluster assignments + risk scores via postMessage
4. Implement src/utils/riskScore.ts – computes cluster risk score using the weighted formula
5. Create src/utils/mitigationRecommender.ts – for each risk factor, generates human-readable diversification recommendations
6. Build src/components/canvas/RiskTopologyMap.tsx – equirectangular canvas map with:
   • Node dots colored by risk tier
   • Cluster convex hull polygons (semi-transparent)
   • Zoom/pan support
   • Click cluster → show risk breakdown and mitigations
7. Create src/components/validators/RiskSummaryPanel.tsx – tabular summary of all clusters with risk score, node count, and top mitigation
8. Mount in a new "Correlation Risk" tab on ValidatorDashboard.tsx

[Mona-i]

Hello,

I'm a Software Developer with more than five years experience. Pull request in a bit if assigned.

[grantfox-oss]

🦊 GrantFox — @Mona-i has been assigned to this issue as part of the Official Campaign campaign!

Next steps:

1. Open a Pull Request referencing this issue (e.g., Closes #57)
2. Your PR will be reviewed by the VeriNode-Labs maintainers

Good luck! Track your progress on GrantFox.