device-fingerprinting-pro 1.1.5

Device fingerprinting library with hardware identification and post-quantum cryptography

Device Fingerprinting Library

A Python library for generating unique device identifiers based on hardware characteristics. Includes post-quantum cryptographic signatures using NIST-standardized algorithms.

Features

• Hardware Detection: CPU, memory, storage, and network interface identification
• Cross-Platform: Windows, macOS, and Linux support
• Post-Quantum Crypto: ML-DSA (Dilithium) signatures via pqcrypto library
• Configurable: Choose which hardware components to include
• Persistent: Device IDs remain stable across software changes

Installation

pip install device-fingerprinting-pro

Quick Start

Basic Usage

from device_fingerprinting import generate_fingerprint

# Generate device fingerprint
fingerprint = generate_fingerprint()
print(f"Device ID: {fingerprint}")

With Post-Quantum Cryptography

from device_fingerprinting import enable_post_quantum_crypto, generate_fingerprint

# Enable quantum-resistant signatures
enable_post_quantum_crypto(algorithm="Dilithium3")

# Generate signed fingerprint
fingerprint = generate_fingerprint()
print(f"Quantum-safe device ID: {fingerprint}")

Custom Configuration

from device_fingerprinting import DeviceFingerprinter

fingerprinter = DeviceFingerprinter(
    include_cpu=True,
    include_memory=True,
    include_storage=True,
    include_network=False,  # Skip network interfaces
    hash_algorithm='sha256'
)

device_id = fingerprinter.generate()

Hardware Components

CPU Information

• Processor model and architecture
• Core count and thread count
• CPU features and instruction sets

Memory Details

• Total physical memory
• Memory module configuration
• Memory type and speed

Storage Devices

• Disk serial numbers and models
• Storage interface types
• Drive capacity and health status

Network Interfaces

• MAC addresses
• Interface types (Ethernet, WiFi, etc.)
• Network adapter hardware IDs

Post-Quantum Cryptography

Supported Algorithms

• ML-DSA (Dilithium): NIST-standardized signature scheme
• Security Levels: NIST Level 3 equivalent
• Key Sizes: 1952/4032 bytes (public/private)
• Signature Size: ~6KB

Implementation Details

from device_fingerprinting import enable_post_quantum_crypto, get_crypto_info

# Enable PQC with specific algorithm
success = enable_post_quantum_crypto(
    algorithm="Dilithium3",
    hybrid_mode=True
)

# Check current crypto configuration
info = get_crypto_info()
print(f"Algorithm: {info['algorithm']}")
print(f"Library: {info['pqc_library']}")
print(f"Quantum Resistant: {info['quantum_resistant']}")

Use Cases

• Device Authentication: Verify device identity for access control
• Software Licensing: Bind licenses to specific hardware configurations
• Fraud Detection: Identify suspicious login attempts from new devices
• Asset Management: Track and inventory computing devices
• Security Auditing: Monitor device changes in enterprise environments

Cross-Platform Support

Platform	CPU	Memory	Storage	Network	Status
Windows	✅	✅	✅	✅	Stable
macOS	✅	✅	✅	✅	Stable
Linux	✅	✅	✅	✅	Stable

Performance

• Generation Time: 50-200ms typical
• Memory Usage: <5MB
• Dependencies: psutil, pqcrypto (optional)
• Caching: Configurable hardware info caching

Requirements

• Python 3.7+
• psutil (for hardware detection)
• pqcrypto (for post-quantum cryptography, optional)

License

MIT License - see LICENSE file for details.

Links

• GitHub Repository
• Issue Tracker
• Documentation