AI-Powered Cyberwar Clock Framework

Dynamic Forecasting of Cyber Conflict Escalation (2025-2036)

Data Input Layer
Threat Evolution Score (TES)
Sophistication, impact, and defensive capabilities with temporal weighting
Geopolitical Tension Index (GTI)
Arms race, sanctions, military posture, treaties, and non-state activity
Cross-Domain Coupling Score (CDCS)
Interdependencies between cyber and physical domains
Technology Acceleration Factor (TAF)
AI, quantum computing, autonomous malware, and deepfake risks
Defensive Countermeasure Index (DCI)
Post-quantum crypto, AI defenses, diplomacy, and infrastructure hardening
AI Forecasting Engine
XGBoost
Pattern Recognition
LSTM
Time Series Analysis
Temporal Fusion
Multi-horizon Forecasting
Ensemble Methods
Robust Predictions
Data Sources:
MITRE ATT&CK • SIPRI • NIST • Threat Intelligence
Historical Incidents • Real-time Feeds
Output & Visualization
Cyberwar Clock Score
Current Risk: 6.2/10
Time to Midnight: 3.8 hours
Latent (0-2.5)
Emerging (2.6-5.0)
Active (5.1-7.5)
Critical (7.6-10)
Scenario Outputs:
  • 12-month forecasts
  • Multi-scenario analysis
  • Policy recommendations
  • Early warning alerts
  • Uncertainty quantification
AI-Powered Forecasting Workflow
1
Data Ingestion
Real-time collection from multiple intelligence sources and historical databases
2
Feature Engineering
Transform raw data into predictive indices using domain expertise
3
AI Training & Validation
Ensemble modeling with cross-validation on historical escalation patterns
4
Scenario Forecasting
Generate probabilistic predictions with uncertainty bounds and policy insights