restalker 2.2.4

Text analyzer package

🕵️ reStalker

IOC and Entities Extractor

A powerful Python library for extracting Indicators of Compromise (IOCs) and various entities from binary or text sources.

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📋 Table of Contents

• 🚀 Quick Start
• 🎯 Detection Capabilities
• 📦 Installation
• 🧠 GLiNER2 Named Entity Recognition
• 💻 Usage Examples
• 🔄 Migration Guide
• 📖 Documentation
• 🤝 Contributing
• 🙏 Acknowledgements

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🚀 Quick Start

import restalker

# Regex-only (fast, no ML model - recommended for IOC extraction)
s = restalker.reStalker(tor=True, i2p=True)
elements = s.parse(input_text)

for element in elements:
    print(f"[*] Darknet IOC found: {element}")

# With GLiNER2 AI (for person names, orgs, locations)
s = restalker.reStalker(use_ner=True, own_name=True, organization=True)
elements = s.parse(input_text)

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🎯 Detection Capabilities

reStalker can extract these entities from any binary or text source:

🔐 Credentials & Identity

• Base64 encoded data (base64=True)
• Username patterns (username=True)
• Password patterns (password=True)
• Phone numbers (phone=True)
• Email addresses (email=True)
• Personal names (own_name=True)
• PGP keys (pgp=True)

🗺️ Location & Organization

• Location information (location=True)
• Organization names (organization=True)
• Keyphrases (keyphrase=True)
• Keywords (keywords=["keyword1", "keyword2"])

📊 Analytics & Tracking

• Google Analytics tracking codes (gatc=True)

💰 Digital Assets & Cryptocurrencies

• BTC (Bitcoin) wallet addresses (btc_wallet=True)
• ETH (Ethereum) wallet addresses (eth_wallet=True)
• XMR (Monero) wallet addresses (xmr_wallet=True)
• ZEC (Zcash) wallet addresses (zec_wallet=True)
• DASH wallet addresses (dash_wallet=True)
• DOT (Polkadot) wallet addresses (dot_wallet=True)
• XRP (Ripple) wallet addresses (xrp_wallet=True)
• BNB (Binance) wallet addresses (bnb_wallet=True)

📱 Social Networks & Communication

• Twitter/X account handles (twitter=True)
• Telegram URLs (telegram=True)
• WhatsApp URLs (whatsapp=True)
• Discord URLs (discord=True)
• Skype URLs (skype=True)
• Tox ID identifiers (tox=True)
• Session ID identifiers (session_id=True)

🔐 Cryptographic Hashes

• MD5 hash values (md5=True)
• SHA1 hash values (sha1=True)
• SHA256 hash values (sha256=True)

💳 Financial Information

• BIN (Bank Identification Numbers) (bin_number=True)
• Credit Card numbers (credit_card=True)
• CCN (Credit Card Numbers - generic) (ccn_number=True)

🌐 Dark Web & Alternative Networks

• Tor (.onion) URLs (tor=True)
• I2P URLs (i2p=True)
• Freenet URLs (freenet=True)
• ZeroNet URLs (zeronet=True)
• BitName URLs (bitname=True)
• IPFS URLs (ipfs=True)

📋 Paste Sites & Code Sharing

• justpaste.it links (paste=True)
• pastebin.com links (paste=True)
• pasted.co links (paste=True)
• hastebin.com links (paste=True)
• snipt.org links (paste=True)
• gist.github.com links (paste=True)
• telegra.ph links (paste=True)
• ghostbin.com links (paste=True)

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📦 Installation

🎯 Quick Start

CPU-only (Default, Recommended for Most Users):

pip install restalker

Or with Poetry:

poetry add restalker

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🚀 GPU Acceleration (Optional)

reStalker supports GPU acceleration for significantly faster entity extraction using GLiNER2. Choose the appropriate installation method based on your hardware:

🔍 Automatic Detection (Recommended)

# Clone or navigate to the repository
git clone https://github.com/junquera/restalker.git
cd restalker

# Detect your GPU hardware
python scripts/detect_gpu.py

# Follow the recommended installation command shown

🎮 Manual GPU Installation

NVIDIA GPU (CUDA 11.8+):

# Using Poetry
poetry install --extras gpu

# Using pip with setup.py
pip install -e .[gpu]

# Using requirements file
pip install -r requirements-gpu-cuda.txt

AMD GPU (ROCm 5.x+, Linux only):

# First, install ROCm: https://rocm.docs.amd.com/

# Using Poetry
poetry install --extras amd-gpu

# Using pip with setup.py
pip install -e .[amd-gpu]

# Using requirements file
pip install -r requirements-gpu-rocm.txt

CPU-only (Explicit):

# Using Poetry
poetry install

# Using pip with requirements file
pip install -r requirements.txt

💾 Disk Space & Performance

Installation	Disk Space	Performance vs CPU	Best For
CPU-only	~500 MB	Baseline (1x)	Most users, portable systems
NVIDIA GPU	~3.2 GB	5-10x faster	Systems with NVIDIA GPUs
AMD GPU	~3.5 GB	3-7x faster	Linux systems with AMD GPUs

✅ Verify GPU Installation

After installing with GPU support, verify it's working:

import torch
print(f"CUDA available: {torch.cuda.is_available()}")
print(f"Device: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'CPU'}")

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🧠 GLiNER2 Named Entity Recognition

reStalker uses GLiNER2 (Generalized Named Entity Recognition v2) for advanced entity extraction. This AI-powered system provides context-aware detection of personal information, organizations, locations, and more.

What is GLiNER2?

GLiNER2 is a state-of-the-art zero-shot Named Entity Recognition model that can identify entities without task-specific training. It understands context and relationships between words, making it highly accurate for extracting:

• Personal names (people mentioned in text)
• Organizations (companies, agencies, groups)
• Locations (cities, countries, addresses)
• Phone numbers (with context validation)
• Email addresses
• Keyphrases (important multi-word expressions)

Model Used

reStalker v2.2.0+ uses the fastino/gliner2-large-v1 model (~340MB):

• 340M parameters for high accuracy
• Optimized for cybersecurity and OSINT use cases
• No TensorFlow dependency required
• Runs efficiently on CPU or GPU
• Only loaded when use_ner=True is set

Enhanced Phone Detection

GLiNER2 includes advanced phone number detection with hex filtering to prevent false positives:

• As of v2.2.2, phone parsing uses stricter EXACT_GROUPING validation in phonenumbers to reduce loosely formatted false positives.

import restalker

# Phone numbers in cryptographic hashes are NOT detected
stalker = restalker.reStalker(phone=True)
text = "Hash: a1b2c3d4567890abcdef"  # Contains "567890" but not a phone
results = stalker.parse(text)
# No phone detected ✓

# Real phone numbers ARE detected
text = "Contact: [phone]"
results = stalker.parse(text)
# Phone detected: [phone] ✓

This enhancement prevents crypto wallet addresses, hashes (MD5, SHA1, SHA256), and hex strings from being incorrectly identified as phone numbers.

Context-Aware Extraction

GLiNER2 validates entity context to ensure accurate extraction:

# Prevents substring matches
text = "[e-mail]"  # "example" is part of email, not a person
stalker = restalker.reStalker(own_name=True, email=True)
results = stalker.parse(text)
# Extracts email, but "example" is not extracted as a name ✓

# Handles multi-line entities
text = """
Name: John
Doe
"""
results = stalker.parse(text)
# Correctly splits "John" and "Doe" as separate entities ✓

Migration from GLiNER v0.2.x

If you're upgrading from reStalker v2.1.x (which used GLiNER v0.2.25), the changes are seamless:

• No API changes - All your existing code works as-is
• Better accuracy - Improved entity detection with fewer false positives
• Faster performance - GLiNER2 is more optimized
• No TensorFlow - Reduced dependencies and installation size

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💻 Usage Examples

Basic Usage (Regex-Only, Fastest)

import restalker

# Regex-only: fast IOC extraction, no ML model loaded
# use_ner defaults to False, so this is equivalent to use_ner=False
stalker = restalker.reStalker(tor=True, i2p=True, btc_wallet=True)

# Parse input text for IOCs
elements = stalker.parse(input_text)

# Process the results
for element in elements:
    print(f"[*] IOC found: {element}")

Advanced Configuration (With GLiNER2 AI)

import restalker

# Enable GLiNER2 for AI-powered entity extraction
# use_ner=True is required for: own_name, organization, location, username, password
stalker = restalker.reStalker(
    use_ner=True,           # Enable GLiNER2 NER model
    own_name=True,          # Person names (requires use_ner=True)
    organization=True,      # Organizations (requires use_ner=True)
    location=True,          # Locations (requires use_ner=True)
    tor=True,               # Tor .onion URLs
    i2p=True,               # I2P URLs
    btc_wallet=True,        # Bitcoin addresses
    eth_wallet=True,        # Ethereum addresses
    email=True,             # Email addresses
    telegram=True,          # Telegram URLs
    base64=True             # Base64 encoded data
)

# Process your data
with open('data.txt', 'r') as f:
    content = f.read()

results = stalker.parse(content)

# Categorize results
for result in results:
    print(f"Type: {result.type}, Value: {result.value}")

Choosing use_ner=True vs use_ner=False

Feature	use_ner=False (default)	use_ner=True
Speed	Fast (no model loading)	Slower (~340MB model loads on first use)
Person Names	Not available	Available (own_name=True)
Organizations	Not available	Available (organization=True)
Locations	Not available	Available (location=True)
Usernames / Passwords	Not available	Available
Phone Detection	Regex-based	Enhanced with GLiNER2 context validation
Memory Usage	~50MB	~400MB
Best For	IOC extraction, crypto wallets, URLs	OSINT, entity extraction, person tracking

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🔄 Migration Guide

Upgrading from v2.1.x to v2.2.x

use_ner Parameter (v2.2.1+)

v2.2.1 adds the optional use_ner parameter (default: False). All existing code continues to work unchanged - you only need to add use_ner=True if you want GLiNER2 AI features.

# v2.1.x code - still works in v2.2.x
stalker = reStalker(phone=True, email=True, btc_wallet=True)

# Explicitly opting into GLiNER2 AI features
stalker = reStalker(use_ner=True, own_name=True, organization=True, location=True)

GLiNER2 Model Download (v2.2.0)

On first use of use_ner=True, reStalker downloads the fastino/gliner2-large-v1 model (~340MB) from HuggingFace. This is a one-time download cached locally.

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📖 Documentation

For comprehensive documentation, examples, and API reference, visit our documentation site.

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🤝 Contributing

We welcome contributions! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

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🙏 Acknowledgements

Byron Labs is an active supporter of the reStalker development.