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GeoWatch Tower (G | W | T)

GeoWatch Tower is a cryptographic geolocation verification and behavioral anomaly detection framework. It generates tamper-proof GeoProofs from device data, validates identity authenticity through per-user DNA Honeypots, and records verified proofs on a public Witness Ledger using Zero-Knowledge Proofs and Merkle hashing.

Here is the GeoWatch Tower MVP Orientation video: GeoWatch Tower (G | W | T)


GeoWatch Tower


Verifiable Digital Presence (VDP)

GeoWatch Tower™ operates at the intersection of:

  1. User & Entity Behavior Analytics (UEBA)
  2. Zero Trust Identity
  3. Cryptographic Verifiability
  4. Open Threat Intelligence

creating a new category: Verifiable Digital Presence (VDP).


Features

Feature Description
GeoProof Engine Generates signed cryptographic proofs of geolocation using Ed25519 keys
DNA Honeypots Invisible behavioral traps per user to detect bots or identity fraud
Public Witness Ledger Tamper-proof proof registry using Merkle Trees and Zero-Knowledge Proofs
Impossible Travel Detection Detects anomalies in user travel using ML and graph clustering
On-Device Security Proofs generated locally with secure enclave or WebAuthn integration
IPFS & Blockchain Ready Can push validated proofs to decentralized ledgers for transparency

Core Technologies

Category Technologies Used
Language Python 3.x
Cryptography cryptography, py-ed25519, pyzeroknp
Machine Learning scikit-learn, pandas, networkx
Geospatial Processing geopy, requests
Storage / Ledger ipfshttpclient, neo4j
Data Handling uuid, json, dataclasses

Set-up

1. Clone the Repository

git clone https://github.com/SakaethRam/GeoWatchTower.git
cd GeoWatch-Tower

2. Install Dependencies

pip install -r requirements.txt

Or install manually:

pip install neo4j geopy requests scikit-learn pandas networkx cryptography ipfshttpclient py-ed25519 pyzeroknp

3. Run the Notebook

jupyter notebook "GeoWatch Tower (v1.0).ipynb"

Docker File

FROM python:3.10-slim

WORKDIR /app

COPY . /app

RUN pip install --no-cache-dir \
    neo4j geopy requests scikit-learn pandas networkx cryptography ipfshttpclient py-ed25519 pyzeroknp

CMD ["jupyter", "notebook", "--ip=0.0.0.0", "--allow-root", "--NotebookApp.token=''", "--NotebookApp.password=''"]

Build Docker Image

docker build -t geowatch-tower .

Run Container

docker run -p 8888:8888 geowatch-tower

Project Structure & Architecture

GeoWatch-Tower/
│
├── GeoWatch Tower (v1.0).ipynb     # Main executable notebook
├── requirements.txt                 # Dependencies list
├── Dockerfile                       # Container setup
│
├── core/
│   ├── geoproof.py                  # Cryptographic GeoProof engine (Ed25519)
│   ├── honeypot.py                  # DNA Honeypot logic
│   ├── anomaly_detector.py          # ML model for travel anomaly detection
│   └── ledger.py                    # Merkle + ZKP ledger integration
│
└── utils/
    ├── geo_utils.py                 # GeoIP & distance utilities
    └── visualization.py             # NetworkX-based cluster visualization

Architecture Overview

 ┌────────────────────────┐
 │   User Device / Node   │
 └──────────┬─────────────┘
            │
        (GeoProof)
            │
 ┌──────────▼─────────────┐
 │  GeoWatch Engine (ML)  │
 └──────────┬─────────────┘
            │
       (Merkle + ZKP)
            │
 ┌──────────▼─────────────┐
 │  Public Witness Ledger │
 └────────────────────────┘

Use Case

Scenario: An enterprise platform wants to prevent account takeovers by verifying that login requests originate from legitimate, geographically consistent devices. GeoWatch Tower ensures each access event carries a signed GeoProof bound to the device, preventing impersonation, spoofing, and synthetic identity attacks.


How It Works

  1. Proof Generation The device captures GPS/IP data and generates a signed proof using Ed25519.
  2. DNA Honeypot Injection Per-user honeypots are deployed to detect automation and mimicry attacks.
  3. Anomaly Detection Travel and access events are analyzed using ML and graph models to detect impossible travel or correlated fraud.
  4. Ledger Validation Valid proofs are recorded to a public Witness Ledger using Merkle Trees and ZK Proofs.
  5. Audit & Visualization Administrators can query the Neo4j/Graph DB to visualize behavioral clusters and anomaly risk.

Contribution

Contributions are welcome. Please fork the repository, make your changes, and submit a pull request. Ensure that all code is properly documented and tested.

Contribution Guidelines

To contribute:

  • Fork the repository.
  • Create a feature branch.
  • Implement your changes.
  • Submit a pull request with a clear description of modifications.

© 2025 GeoWatch Tower | Secure ARKIN X Standard v1.0

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