CHANAKYA Framework - Multi-Layer OPSEC Attribution Analysis

Framework Overview

CHANAKYA is a research-grade framework for understanding how operational security fails through emergent signal correlation across abstraction layers.

Unlike traditional OPSEC checklists, CHANAKYA models how weak signals combine to create strong attribution through:

Multi-layer signal analysis (Technical + Intelligence disciplines)
AI/ML-augmented correlation (Graph ML, LSTMs, behavioral clustering)
Quantitative signal scoring (V × R × C attribution formula)
Cross-INT fusion (OSINT + GEOINT + HUMINT + SIGINT)

Technical Layers

🌐

Browser Layer

WebRTC IP leaks, Canvas fingerprinting, font enumeration, JS timing attacks

CRITICAL

WebRTC Leak Canvas (99.9% unique) Font Enum

💻

Userland Layer

Binary fingerprints, TLS signatures, environment leaks, timezone artifacts

HIGH

Binary Entropy TLS Fingerprint Env Leaks

⚙️

Kernel-Adjacent

Syscall patterns, timing side-channels, scheduler behavior, workload classification

MEDIUM

Syscall Timing ML Workload ID

🌍

DNS Layer

Resolver correlation, sinkhole detection, query patterns, passive DNS graphs

CRITICAL

Sinkhole Detection Passive DNS TTL Analysis

🔀

Routing Layer

BGP announcements, AS-path analysis, route asymmetry, traffic localization

HIGH

BGP Correlation AS-Path

⏰

Metadata/Temporal

Activity timing, operational cadence, behavioral entropy, timezone triangulation

CRITICAL

Timing Patterns Entropy Analysis

Intelligence Layers (Multi-INT)

🔍

OSINT

GitHub mining, LinkedIn inference, WHOIS correlation, conference tracking

CRITICAL

GitHub Commits Code Stylometry

📡

SIGINT

Traffic analysis, cellular correlation, IMSI catchers, Tor flow correlation

HIGH

IMSI Catcher Traffic ML

🗺️

GEOINT

Timezone triangulation, IP geolocation, satellite imagery, cell tower data

HIGH

Bayesian TZ Fusion Satellite Imagery

👥

HUMINT

Behavioral profiling, cultural indicators, conference attendance, social engineering

CRITICAL

Badge Photos Work/Life Patterns

🔬

Forensics

Filesystem analysis, memory forensics, EXIF extraction, timeline reconstruction

HIGH

EXIF GPS MAC Times

AI-Era Enhancements (2026)

AI-Augmented Attribution: How Graph ML, LSTMs, and LLMs enable retrospective attribution
Quantitative Signal Scoring: V × R × C formula (Visibility × Retention × Correlation)
Behavioral Entropy: Shannon entropy H > 3.5 bits target for unpredictability
Counter-AI OPSEC: Defensive techniques against ML correlationli>
Retrospective Attribution: How "safe" signals become dangerous years later

Read Full AI-Era Analysis →

Quick Start

1. Personal OPSEC Audit

Personal OPSEC Checklist →

5-minute pre-operation checklist: WebRTC leaks, DNS leaks, browser fingerprinting, temporal patterns

2. Framework Analysis

Run framework analyzers:

python examples/opsec_audit_example.py

3. Simulations

See how OPSEC fails:

python simulations/ai-era/retrospective-attribution.py

Framework Overview

Technical Layers

Intelligence Layers (Multi-INT)

AI-Era Enhancements (2026)

Quick Start

1. Personal OPSEC Audit

2. Framework Analysis

3. Simulations

Documentation