CryptGuardian is a research-level concept, created from the perspective of a developer concerned about long-term security for distributed systems. It is not a final product, but an exploration of defense strategies against modern and future threats, including AI-assisted attacks and quantum cryptanalysis.
The objective is simple: Increase the cost, complexity, and feasibility barrier for attackers. No system is unbreakable — but it can be made impractical to break.
Version: v0.3-alpha (Quantum Leap) Copyright © 2025 NCF
Modern cryptography faces two accelerating risks:
- AI-based automated attacks
- Quantum algorithms that weaken classical cryptosystems
- Read Mathematical Hallucination
CryptGuardian explores mitigation strategies through:
- Quantum-hardened hashing
- Post-Quantum signature simulation
- Timing anomaly detection
- Threat intelligence and pattern analysis
- Safer block validation and monitoring
This project is an idea to learn who you are, a technical experiment — not a guaranteed quantum-safe framework.
The default block type applies:
- SHA-256 → SHA3-512 → SHA-256 (double-round hashing)
- 32-byte nonces (twice the classical size)
- Structure hardened against Grover-like search acceleration
If installed, BLAKE3 provides:
- Higher performance
- Wider internal state
- Tree-based hashing
Requires:
pip install blake3A simplified Lamport One-Time Signature model demonstrates:
- Large PQC key sizes
- One-time usage constraints
- Binary-path signature generation
- Hash-based verification
This prepares the groundwork for future Dilithium/Kyber integration.
The following classes and functions are the core elements of the Quantum Leap strategy, implementing hybrid hashing and Post-Quantum Signatures:
# Quantum-Resistant Enhancements for CryptGuardian
# Adds BLAKE3 + Hybrid PQC approach
import hashlib
from typing import Dict, Tuple
import secrets
import time
import binascii
# ============================================================
# OPTION 1: Hybrid Hash Chain (A simple, immediate PQC solution)
# ============================================================
class QuantumResistantBlock:
"""Drop-in replacement for your existing Block class with Quantum-Hardening"""
def __init__(self, index: int, prev_hash: str, timestamp: float, data: str, nonce: str = None):
self.index = index
self.prev_hash = prev_hash
self.timestamp = timestamp
self.data = data
# Doubled Nonce length (64 chars / 32 bytes) for increased quantum difficulty
self.nonce = nonce or secrets.token_hex(32)
self.hash = self.calculate_quantum_hash()
self.validation_score = 0.0
self.metadata = {'quantum_resistant': True}
def calculate_quantum_hash(self) -> str:
"""
Hybrid Hashing: SHA-256 + SHA3-512 + Double-Round
"""
block_string = f"{self.index}{self.prev_hash}{self.timestamp}{self.data}{self.nonce}"
# Round 1: SHA-256
hash1 = hashlib.sha256(block_string.encode()).hexdigest()
# Round 2: SHA3-512 over the SHA-256 Result
hash2 = hashlib.sha3_512(hash1.encode()).hexdigest()
# Final: Combined Hash (SHA-256 over both results)
return hashlib.sha256(f"{hash1}{hash2}".encode()).hexdigest()
# ============================================================
# OPTION 2: BLAKE3 Integration (fast and quantum-safe hash)
# ============================================================
# Installation: pip install blake3
try:
import blake3
class BLAKE3Block:
"""High-Performance Quantum-Hardened Block, using the modern BLAKE3 hash."""
def __init__(self, index: int, prev_hash: str, timestamp: float, data: str):
self.index = index
self.prev_hash = prev_hash
self.timestamp = timestamp
self.data = data
self.nonce = secrets.token_hex(32) # Standardized long nonce
self.hash = self.calculate_blake3_hash()
def calculate_blake3_hash(self) -> str:
"""BLAKE3 is fast and designed for modern, multi-core systems."""
block_string = f"{self.index}{self.prev_hash}{self.timestamp}{self.data}{self.nonce}"
return blake3.blake3(block_string.encode()).hexdigest()
except ImportError:
BLAKE3Block = None
# ============================================================
# OPTION 3: Post-Quantum Signature Simulation (Lamport Principle)
# ============================================================
class PQCBlockchain:
"""Simulates Post-Quantum Signatures based on the Lamport One-Time Signature principle."""
def __init__(self):
self.chain = []
self.quantum_keys = self._generate_quantum_keypair()
def _generate_quantum_keypair(self) -> Dict:
"""Generates a Lamport One-Time Signature Keypair (16KB key size simulated)."""
private_key = [[secrets.token_bytes(32) for _ in range(2)] for _ in range(256)]
public_key = [[hashlib.sha256(k).digest() for k in pair] for pair in private_key]
return {
'private': private_key,
'public': public_key,
'usage_count': 0,
'max_uses': 100
}
def sign_block(self, block_hash: str) -> bytes:
"""Creates the Quantum-Resistant Signature."""
if self.quantum_keys['usage_count'] >= self.quantum_keys['max_uses']:
self.quantum_keys = self._generate_quantum_keypair()
hash_bits = bin(int(block_hash, 16))[2:].zfill(256)
signature = b''.join(
self.quantum_keys['private'][i][int(bit)]
for i, bit in enumerate(hash_bits)
)
self.quantum_keys['usage_count'] += 1
return signature
def verify_signature(self, block_hash: str, signature: bytes, public_key: list) -> bool:
"""Verifies the PQC signature against the public key."""
hash_bits = bin(int(block_hash, 16))[2:].zfill(256)
for i, bit in enumerate(hash_bits):
sig_chunk = signature[i*32:(i+1)*32]
expected_hash = public_key[i][int(bit)]
if hashlib.sha256(sig_chunk).digest() != expected_hash:
return False
return True
# ... (Integration and Benchmarking Functions follow in the full code)This module provides an analytical defense against latency-based attacks or anomalies in the execution time of block operations, which can indicate malicious activity or resource exhaustion.
import numpy as np
from typing import List, Tuple
def calculate_anomaly_zscore(
historical_times: List[float],
new_time_measurement: float,
threshold: float = 3.0
) -> Tuple[float, bool]:
"""
Calculates the Z-Score for a new measurement relative to historical data
and checks if it exceeds a specified anomaly threshold (default: 3.0).
"""
if len(historical_times) < 2:
return 0.0, False
mu = np.mean(historical_times)
sigma = np.std(historical_times)
if sigma == 0:
return 0.0, False
# Z = (Value - Mean) / Standard Deviation
z_score = (new_time_measurement - mu) / sigma
is_anomaly = abs(z_score) > threshold
return z_score, is_anomaly
# --- Demo Usage (Snippet) ---
# past_times = [0.051, 0.049, 0.050, 0.052, 0.048, 0.050, 0.051, 0.049]
# slow_time = 0.065
# z_slow, anomaly_slow = calculate_anomaly_zscore(past_times, slow_time)
# # Expected: Anomaly: True- Hybrid block hashing with increased post-quantum resistance
- BLAKE3 option for high-performance blockchain hashing
- Lamport-inspired signature simulation
- Defensive timing analytics
- Detection of common attack vectors
- Simplified anomaly and replay detection logic
- Thread-safe design ideas for production networks
This is a conceptual research tool, not a finished product.
git clone https://github.com/NemesisCyberForce/CryptGuardian.git
cd CryptGuardian
pip install blake3 # optional
python cryptguardian_enhanced.pyfrom cryptguardian_enhanced import SmartBlockchain, enhanced_alert_handler
blockchain = SmartBlockchain()
blockchain.guardian.register_alert_handler(enhanced_alert_handler)
blockchain.add_block("Normal transaction")
blockchain.add_block("SELECT * FROM users; DROP TABLE users;--")Start the full suite:
python cryptguardian_enhanced.pyExpected outcomes include blocked injections, anomaly flags, timestamp skew detection, and replay defense.
- Proper PQC Signatures (Dilithium)
- Kyber KEM for secure symmetric key exchange
- Optional machine learning anomaly detection
- Async engine and distributed validation
- Multiple consensus mode support (PoW/PoS/PBFT)
Apache License 2.0
Use only for legal research and defensive analysis.
This is an alpha-stage idea built by a developer exploring how to harden systems against future threats. Not production-ready. Not guaranteed secure. It is a research exercise in resilience, not a final solution.
