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CHANAKYA is a research-grade framework for understanding how operational security fails through emergent signal correlation across abstraction layers.
知己知彼，百战不殆
"Know yourself and know your enemy, and you will never be defeated in a hundred battles." — Sun Tzu
CHANAKYA is not an OPSEC checklist or compliance framework. It is a multi-INT intelligence analysis platform that models how weak signals combine across 15 dimensions to enable attribution.
OPSEC failures emerge from signal correlation, not checklist violations.
15 Comprehensive OPSEC Dimensions:
| Category | Layers | Purpose |
|---|---|---|
| Technical | 6 layers | Browser, Userland, Kernel, DNS, Routing, Metadata |
| Intelligence | 6 layers | OSINT, SIGINT, GEOINT, HUMINT, Forensics, AI-Augmented |
| Operational | 3 layers | Anti-Forensics, Financial Privacy, Infrastructure Stealth |
- Philosophy - Core principles & threat philosophy
- Threat Model - Adversary capabilities (Tier 0-3.5)
- OPSEC Failure Taxonomy - 50+ failure mode classification
- Signal Scoring Methodology - V×R×C quantitative formula
- Browser OPSEC - WebRTC leaks, Canvas fingerprinting
- DNS Layer - Resolver correlation, sinkhole detection
- Routing Layer - BGP, AS-path analysis
- Metadata & Temporal - Activity timing, behavioral entropy
- OSINT Correlation - GitHub mining, LinkedIn inference
- SIGINT Attribution - Traffic analysis, cellular tracking
- GEOINT Geospatial - Timezone triangulation, satellite imagery
- HUMINT Social Engineering - Behavioral profiling, conferences
- Forensics Attribution - EXIF, filesystem, memory analysis
- Anti-Forensics - HiddenVM, amnesic OS, plausible deniability
- Financial Privacy - Monero, CoinJoin, blockchain analysis
- Infrastructure Stealth - Redirectors, Shodan evasion, domain aging
- AI-Augmented Attribution - Graph ML, LSTMs, retrospective correlation
- Behavioral Entropy - Shannon entropy quantification
- Counter-AI OPSEC - Defensive techniques vs machine learning
- Personal OPSEC Checklist - Military-grade operational manual
- Test Suite - Attribution scenarios & pre-operation audit
- Contributing Guide - Git workflow & development standards
- Intelligence Analysts - All-source fusion & attribution
- Red Team Operators - Understanding infrastructure leakage
- Security Researchers - Attribution technique research
- Military Cyber Operations - Nation-state resistance
AW = V × R × C
- V = Visibility (0.0-1.0) - How observable is the signal?
- R = Retention (0.0-1.0) - How long is it logged/stored?
- C = Correlation (0.0-1.0) - How linkable to other signals?
Risk Tiers:
- AW > 0.8: CRITICAL (immediate attribution likely)
- AW 0.5-0.8: HIGH (attribution probable with analysis)
- AW 0.3-0.5: MEDIUM (requires cross-INT correlation)
- AW < 0.3: LOW (weak signal, difficult to attribute alone)
- Understand the Philosophy → Read Philosophy
- Model Your Threats → Review Threat Model
-
Run Personal Audit →
python tests/personal_opsec_audit.py -
Analyze Attribution →
python tests/test_attribution_scenarios.py - Explore Layers → Navigate to specific INT discipline above
- Repository: https://github.com/bb1nfosec/chanakya-opsec
- Interactive Wiki: https://bb1nfosec.github.io/chanakya-opsec/ (once Pages enabled)
- License: MIT (Research & Education Only)
See Contributing Guide for:
- Git workflow & branch conventions
- Documentation standards
- Pull request template
- Testing requirements
Кто владеет информацией, тот владеет миром
"Who controls information, controls the world."
CHANAKYA: Where signals converge, attribution emerges.