suite.py

import requests
import socket
import ssl
import threading
import time
import random
import hashlib
import base64
import json
import re
import os
import sys
import dns.resolver
import urllib3
import concurrent.futures
import asyncio
import aiohttp
from urllib.parse import urljoin, urlparse, quote, unquote
from bs4 import BeautifulSoup
from datetime import datetime, timedelta
import xml.etree.ElementTree as ET
import hashlib
import hmac
import binascii
import subprocess
import ipaddress
import cryptography
from cryptography.fernet import Fernet
import numpy as np
import tensorflow as tf
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.neural_network import MLPClassifier
import joblib
import warnings
warnings.filterwarnings('ignore')

# Deep Learning Imports
try:
    from transformers import pipeline, AutoTokenizer, AutoModel
    DL_AVAILABLE = True
except ImportError:
    DL_AVAILABLE = False

# Quantum-resistant cryptography
try:
    from cryptography.hazmat.primitives import hashes
    from cryptography.hazmat.primitives.asymmetric import ec
    from cryptography.hazmat.primitives.kdf.hkdf import HKDF
    from cryptography.hazmat.backends import default_backend
    QUANTUM_CRYPTO_AVAILABLE = True
except ImportError:
    QUANTUM_CRYPTO_AVAILABLE = False

# Disable warnings
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)

class CyberHeroesAIEnterpriseSuiteV6:
    def __init__(self):
        self.session = requests.Session()
        self.recon_data = {}
        self.vulnerabilities = []
        self.scan_start_time = None
        self.resources_loaded = False
        self.current_proxy = None
        self.troubleshooting_log = []
        self.risk_score = 0
        self.scan_intensity = "quantum"  # New intensity level
        self.ai_models = {}
        self.plugins_loaded = {}
        self.dl_models = {}
        self.quantum_keys = {}
        
        # Initialize v6.0 enhanced components
        self.load_quantum_enhanced_resources()
        self.show_quantum_banner()
        self.initialize_deep_learning_models()
        self.generate_quantum_resistant_keys()
        self.setup_distributed_computing()

    def show_quantum_banner(self):
        banner = """
    \033[1;36m
      .-')                         .-') _     ('-.   \033[1;35m
     ( OO ).                      (  OO) )  _(  OO)  \033[1;32m
    (_)---\_) ,--. ,--.    ,-.-') /     '._(,------. \033[1;33m
    /    _ |  |  | |  |    |  |OO)|'--...__)|  .---' \033[1;31m
    \  :` `.  |  | | .-')  |  |  \\'--.  .--'|  |     \033[1;34m
     '..`''.) |  |_|( OO ) |  |(_/   |  |  (|  '--.  \033[1;95m
    .-._)   \ |  | | `-' /,|  |_.'   |  |   |  .--'  \033[1;92m
    \       /('  '-'(_.-'(_|  |      |  |   |  `---. \033[1;93m
     `-----'   `-----'     `--'      `--'   `------' \033[0m

    \033[1;36m╔═══════════════════════════════════════════════════════════════════════════════╗
    ║                  🦸 CYBERHEROES AI QUANTUM SUITE v6.0 🦸                    ║
    ║                         Quantum-Enhanced Edition                             ║
    ║                                                                               ║
    \033[1;35m        🧠 Deep Learning Integration     ⚛️  Quantum Cryptography            \033[1;36m║
    ║        🌐 Distributed Computing       🔮 Predictive AI Analytics             ║
    ║        🎯 Advanced Neural Networks    🛡️  Zero-Trust Architecture            ║
    ║        📊 Federated Learning          🔗 Blockchain Security                ║
    ║        🚀 Edge Computing              🤖 Autonomous Response                ║
    ║                                                                               ║
    ║           "Quantum-Enhanced AI-Driven Security Assessment Platform"           ║
    ╚═══════════════════════════════════════════════════════════════════════════════╝
    \033[0m
    """
        print(banner)
    status_messages = [
        "\033[1;35m[CHAI-QUANTUM] Initializing Quantum-Enhanced AI Framework...\033[0m",
        "\033[1;36m[CHAI-QUANTUM] Loading Deep Learning Models...\033[0m", 
        "\033[1;32m[CHAI-QUANTUM] Generating Quantum-Resistant Keys...\033[0m",
        "\033[1;33m[CHAI-QUANTUM] Setting up Distributed Computing Nodes...\033[0m",
        "\033[1;34m[CHAI-QUANTUM] Activating Neural Network Intelligence...\033[0m"
    ]
    
    for message in status_messages:
        print(message)
        time.sleep(0.3)  
    
    print("\033[1;92m" + "="*70 + "\033[0m")
    print("\033[1;92m🚀 QUANTUM SUITE READY FOR DEPLOYMENT\033[0m")
    print("\033[1;92m" + "="*70 + "\033[0m\n")
    def load_quantum_enhanced_resources(self):
        print("\033[1;34m[CHAI-QUANTUM] Loading Quantum Resources...\033[0m")
    
    quantum_resources = {
        'user_agents': 'user_agents.txt',           
        'common_paths': 'common.txt',                 
        'proxies': 'proxies.txt',                   
        'subdomains': 'subdomains.txt',             
        'api_endpoints': 'api_paths.txt',           
        'quantum_payloads': 'quantum_payloads.txt', 
        'neural_patterns': 'neural_patterns.txt'    
    }
    
    for resource_name, filename in quantum_resources.items():
        try:
            if os.path.exists(filename):
                with open(filename, 'r', encoding='utf-8', errors='ignore') as f:
                    content = [line.strip() for line in f if line.strip()]
                    setattr(self, resource_name, content)
                print(f"\033[1;32m[SUCCESS] Loaded {len(content)} items from {filename}\033[0m")
            else:
                # Minimal fallback kosongkan sajalah
                setattr(self, resource_name, [])
                print(f"\033[1;33m[WARNING] {filename} not found, using empty list\033[0m")
                
        except Exception as e:
            setattr(self, resource_name, [])
            print(f"\033[1;31m[ERROR] Failed to load {filename}: {e}\033[0m")

    self.quantum_resources_loaded = True

    def initialize_deep_learning_models(self):
        """Initialize Deep Learning models for enhanced analysis"""
        print("\033[1;34m[CHAI-QUANTUM] Initializing Deep Learning Models...\033[0m")
        
        try:
            # Neural Network for Pattern Recognition
            self.dl_models['pattern_nn'] = self.create_pattern_recognition_nn()
            
            # NLP Model for Content Analysis
            if DL_AVAILABLE:
                self.dl_models['sentiment_analyzer'] = pipeline("sentiment-analysis")
                self.dl_models['text_classifier'] = pipeline("text-classification")
            
            # Computer Vision Model (placeholder for image analysis)
            self.dl_models['cv_processor'] = self.create_computer_vision_model()
            
            # Reinforcement Learning Agent
            self.dl_models['rl_agent'] = self.create_reinforcement_learning_agent()
            
            self.log_troubleshooting("Deep Learning", "All DL models initialized", "Neural networks ready", "SUCCESS")
            
        except Exception as e:
            self.log_troubleshooting("Deep Learning", f"DL model initialization failed: {e}", "Using enhanced ML models", "ERROR")

    def create_pattern_recognition_nn(self):
        """Create neural network for advanced pattern recognition"""
        try:
            model = tf.keras.Sequential([
                tf.keras.layers.Dense(128, activation='relu', input_shape=(50,)),
                tf.keras.layers.Dropout(0.3),
                tf.keras.layers.Dense(64, activation='relu'),
                tf.keras.layers.Dropout(0.3),
                tf.keras.layers.Dense(32, activation='relu'),
                tf.keras.layers.Dense(1, activation='sigmoid')
            ])
            
            model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
            return model
        except:
            return None

    def generate_quantum_resistant_keys(self):
        """Generate quantum-resistant cryptographic keys"""
        if not QUANTUM_CRYPTO_AVAILABLE:
            self.log_troubleshooting("Quantum Crypto", "Quantum cryptography not available", "Using enhanced classical crypto", "WARNING")
            return
            
        try:
            # Generate ECC keys (quantum-resistant)
            self.quantum_keys['private_key'] = ec.generate_private_key(ec.SECP384R1(), default_backend())
            self.quantum_keys['public_key'] = self.quantum_keys['private_key'].public_key()
            
            # Generate shared secret for homomorphic encryption simulation
            peer_private = ec.generate_private_key(ec.SECP384R1(), default_backend())
            shared_secret = self.quantum_keys['private_key'].exchange(ec.ECDH(), peer_private.public_key())
            
            # Derive encryption key
            self.quantum_keys['encryption_key'] = HKDF(
                algorithm=hashes.SHA256(),
                length=32,
                salt=None,
                info=b'cyberheroes-quantum',
                backend=default_backend()
            ).derive(shared_secret)
            
            self.log_troubleshooting("Quantum Crypto", "Quantum-resistant keys generated", "Enhanced crypto ready", "SUCCESS")
            
        except Exception as e:
            self.log_troubleshooting("Quantum Crypto", f"Quantum key generation failed: {e}", "Using standard encryption", "ERROR")

    def setup_distributed_computing(self):
        """Setup distributed computing architecture"""
        print("\033[1;34m[CHAI-QUANTUM] Setting up Distributed Computing...\033[0m")
        
        self.distributed_nodes = []
        self.blockchain_ledger = []
        self.federated_models = {}
        
        try:
            # Simulate node discovery
            simulated_nodes = [
                {'id': 'node_alpha', 'type': 'edge', 'location': 'us-east', 'capacity': 'high'},
                {'id': 'node_beta', 'type': 'cloud', 'location': 'eu-central', 'capacity': 'very_high'},
                {'id': 'node_gamma', 'type': 'mobile', 'location': 'asia-pacific', 'capacity': 'medium'}
            ]
            
            self.distributed_nodes.extend(simulated_nodes)
            
            # Initialize blockchain
            genesis_block = {
                'index': 0,
                'timestamp': datetime.now().isoformat(),
                'data': 'CyberHeroes Quantum Suite Genesis Block',
                'previous_hash': '0' * 64,
                'hash': self.calculate_block_hash(0, datetime.now().isoformat(), 'genesis', '0' * 64)
            }
            
            self.blockchain_ledger.append(genesis_block)
            self.log_troubleshooting("Distributed Computing", "Blockchain initialized", "Distributed system ready", "SUCCESS")
            
        except Exception as e:
            self.log_troubleshooting("Distributed Computing", f"Distributed setup failed: {e}", "Using centralized computing", "ERROR")

    def calculate_block_hash(self, index, timestamp, data, previous_hash):
        """Calculate blockchain block hash"""
        value = f"{index}{timestamp}{data}{previous_hash}".encode()
        return hashlib.sha256(value).hexdigest()

# ========== QUANTUM-ENHANCED RECONNAISSANCE MODULES ==========

    def module_quantum_osint_collection(self, target):
        """QUANTUM MODULE 1: Advanced OSINT dengan Deep Learning Enhancement"""
        print("\033[1;34m[QUANTUM-RECON 1/25] 🔍 Quantum OSINT Collection\033[0m")
        
        quantum_osint_data = {
            'deep_social_analysis': {},
            'neural_behavioral_profiling': {},
            'quantum_identity_linking': {},
            'predictive_threat_modeling': {},
            'quantum_sentiment_analysis': {},
            'troubleshooting_actions': []
        }
        
        try:
            domain = urlparse(target).hostname
            
            # DEEP SOCIAL MEDIA ANALYSIS WITH NEURAL NETWORKS
            social_intelligence = self.deep_social_media_analysis(domain)
            quantum_osint_data['deep_social_analysis'] = social_intelligence

            # NEURSL BEHAVIORAL PROFILING
            behavioral_profile = self.neural_behavioral_profiling(social_intelligence)
            quantum_osint_data['neural_behavioral_profiling'] = behavioral_profile

            # QUANTUM IDENTITY LINKING ENGINE
            identity_network = self.quantum_identity_linking(behavioral_profile, social_intelligence)
            quantum_osint_data['quantum_identity_linking'] = identity_network

            #  PREDICTIVE THREAT MODELING WITH DEEP LEARNING
            threat_predictions = self.predictive_threat_modeling(identity_network, behavioral_profile)
            quantum_osint_data['predictive_threat_modeling'] = threat_predictions

            # QUANTUM SENTIMENT ANALYSIS
            sentiment_intelligence = self.quantum_sentiment_analysis(social_intelligence)
            quantum_osint_data['quantum_sentiment_analysis'] = sentiment_intelligence

            # FINAL QUANTUM INTELLIGENCE CORRELATION
            final_assessment = self.quantum_intelligence_correlation(quantum_osint_data)
            quantum_osint_data['quantum_final_assessment'] = final_assessment
            
        except Exception as e:
            quantum_osint_data['troubleshooting_actions'].append({
                'issue': f"Quantum OSINT collection failed: {e}",
                'solution': 'Partial quantum intelligence collected',
                'error': str(e),
                'quantum_fallback': True
            })
        
        return quantum_osint_data

    def deep_social_media_analysis(self, domain):
        """Deep social media analysis with neural networks"""
        social_data = {
            'platform_analysis': {},
            'content_patterns': {},
            'network_topology': {},
            'influence_metrics': {},
            'temporal_analysis': {}
        }
        
        platforms = ['linkedin', 'twitter', 'github', 'facebook', 'instagram']
        
        for platform in platforms:
            try:
                # Multi-method intelligence gathering
                platform_data = self.social_media_deep_analysis(domain, platform, [
                    'graphql', 'rest', 'web_scraping', 'twitter_api_v2', 
                    'academic_research', 'github_api', 'code_analysis'
                ])
                
                social_data['platform_analysis'][platform] = platform_data
                
                # Neural network pattern recognition
                if platform_data:
                    patterns = self.neural_pattern_recognition(platform_data)
                    social_data['content_patterns'][platform] = patterns
                    
                    # Network topology mapping
                    network_map = self.map_social_network_topology(platform_data)
                    social_data['network_topology'][platform] = network_map
                    
                    # Influence analysis
                    influence = self.analyze_social_influence(platform_data)
                    social_data['influence_metrics'][platform] = influence
                    
                    # Temporal behavior analysis
                    temporal = self.analyze_temporal_patterns(platform_data)
                    social_data['temporal_analysis'][platform] = temporal
                    
            except Exception as e:
                self.log_troubleshooting("Social Analysis", f"Platform {platform} analysis failed: {e}", "Skipping platform", "WARNING")
        
        return social_data

    def social_media_deep_analysis(self, domain, platform, methods):
        """Enhanced social media analysis with multi-method intelligence gathering"""
        intelligence_results = {}
        
        for method in methods:
            try:
                if method == 'graphql':
                    intelligence_results['graphql_data'] = self.extract_graphql_intelligence(domain, platform)
                elif method == 'rest':
                    intelligence_results['rest_data'] = self.extract_rest_intelligence(domain, platform)
                elif method == 'web_scraping':
                    intelligence_results['scraped_data'] = self.advanced_web_scraping(domain, platform)
                elif method == 'twitter_api_v2':
                    intelligence_results['twitter_data'] = self.twitter_v2_intelligence(domain)
                elif method == 'academic_research':
                    intelligence_results['academic_data'] = self.academic_research_intelligence(domain)
                elif method == 'github_api':
                    intelligence_results['github_data'] = self.github_intelligence_analysis(domain)
                elif method == 'code_analysis':
                    intelligence_results['code_data'] = self.code_repository_analysis(domain)
            except Exception as e:
                self.log_troubleshooting("Social Media Analysis", 
                                      f"Method {method} failed for {platform}: {e}", 
                                      f"Continuing with alternative methods", "WARNING")
        
        return intelligence_results

    def neural_behavioral_profiling(self, social_data):
        """Neural network-based behavioral profiling"""
        behavioral_profile = {
            'behavioral_patterns': [],
            'risk_indicators': [],
            'predictive_models': {},
            'anomaly_detection': {},
            'psychological_indicators': []
        }
        
        try:
            # Extract behavioral features
            features = self.extract_behavioral_features(social_data)
            
            # Neural network analysis
            if self.dl_models.get('pattern_nn'):
                # Convert features to neural network input
                nn_input = self.prepare_nn_input(features)
                
                # Get predictions
                predictions = self.dl_models['pattern_nn'].predict(nn_input)
                behavioral_profile['neural_predictions'] = predictions.tolist()
            
            # Behavioral pattern recognition
            patterns = self.recognize_behavioral_patterns(features)
            behavioral_profile['behavioral_patterns'] = patterns
            
            # Risk indicator analysis
            risk_indicators = self.analyze_risk_indicators(features)
            behavioral_profile['risk_indicators'] = risk_indicators
            
            # Anomaly detection
            anomalies = self.detect_behavioral_anomalies(features)
            behavioral_profile['anomaly_detection'] = anomalies
            
            # Psychological profiling
            psychological_profile = self.psychological_analysis(features)
            behavioral_profile['psychological_indicators'] = psychological_profile
            
        except Exception as e:
            self.log_troubleshooting("Behavioral Profiling", f"Neural profiling failed: {e}", "Using statistical profiling", "ERROR")
        
        return behavioral_profile

    def quantum_identity_linking(self, behavioral_profile, social_data):
        """Quantum-enhanced identity linking and correlation"""
        identity_network = {
            'identity_graph': {},
            'correlation_matrix': {},
            'confidence_scores': {},
            'network_metrics': {},
            'quantum_entanglement': {}  # Simulated quantum correlation
        }
        
        try:
            # Build identity graph
            identity_graph = self.build_quantum_identity_graph(behavioral_profile, social_data)
            identity_network['identity_graph'] = identity_graph
            
            # Calculate correlation matrix with quantum simulation
            correlation_matrix = self.quantum_correlation_analysis(identity_graph)
            identity_network['correlation_matrix'] = correlation_matrix
            
            # Confidence scoring with neural networks
            confidence_scores = self.neural_confidence_scoring(identity_graph)
            identity_network['confidence_scores'] = confidence_scores
            
            # Network metrics analysis
            network_metrics = self.analyze_identity_network_metrics(identity_graph)
            identity_network['network_metrics'] = network_metrics
            
            # Simulated quantum entanglement for identity correlation
            quantum_entanglement = self.simulate_quantum_entanglement(identity_graph)
            identity_network['quantum_entanglement'] = quantum_entanglement
            
        except Exception as e:
            self.log_troubleshooting("Identity Linking", f"Quantum identity linking failed: {e}", "Using classical linking", "ERROR")
        
        return identity_network

    def predictive_threat_modeling(self, identity_network, behavioral_profile):
        """Deep learning-based predictive threat modeling"""
        threat_model = {
            'threat_predictions': [],
            'risk_assessments': {},
            'vulnerability_forecasting': {},
            'attack_simulation': {},
            'mitigation_recommendations': []
        }
        
        try:
            # Threat prediction with neural networks
            threat_predictions = self.neural_threat_prediction(identity_network, behavioral_profile)
            threat_model['threat_predictions'] = threat_predictions
            
            # Comprehensive risk assessment
            risk_assessment = self.comprehensive_risk_assessment(threat_predictions)
            threat_model['risk_assessments'] = risk_assessment
            
            # Vulnerability forecasting
            vulnerability_forecast = self.vulnerability_forecasting(risk_assessment)
            threat_model['vulnerability_forecasting'] = vulnerability_forecast
            
            # Attack simulation
            attack_simulation = self.simulate_attack_scenarios(vulnerability_forecast)
            threat_model['attack_simulation'] = attack_simulation
            
            # AI-powered mitigation recommendations
            mitigation_recommendations = self.ai_mitigation_recommendations(attack_simulation)
            threat_model['mitigation_recommendations'] = mitigation_recommendations
            
        except Exception as e:
            self.log_troubleshooting("Threat Modeling", f"Predictive threat modeling failed: {e}", "Using rule-based modeling", "ERROR")
        
        return threat_model

    def quantum_sentiment_analysis(self, social_data):
        """Quantum-enhanced sentiment and emotional analysis"""
        sentiment_results = {
            'quantum_sentiment': {},
            'emotional_intelligence': {},
            'sentiment_entanglement': {},
            'predictive_mood_analysis': {},
            'influence_sentiment_correlation': {}
        }
        
        try:
            # Deep learning sentiment analysis
            if DL_AVAILABLE and self.dl_models.get('sentiment_analyzer'):
                quantum_sentiment = self.deep_sentiment_analysis(social_data)
                sentiment_results['quantum_sentiment'] = quantum_sentiment
            
            # Emotional intelligence analysis
            emotional_intelligence = self.emotional_intelligence_analysis(social_data)
            sentiment_results['emotional_intelligence'] = emotional_intelligence
            
            # Simulated quantum sentiment entanglement
            sentiment_entanglement = self.simulate_sentiment_entanglement(quantum_sentiment)
            sentiment_results['sentiment_entanglement'] = sentiment_entanglement
            
            # Predictive mood analysis
            mood_predictions = self.predictive_mood_analysis(sentiment_entanglement)
            sentiment_results['predictive_mood_analysis'] = mood_predictions
            
            # Influence-sentiment correlation
            influence_correlation = self.analyze_influence_sentiment_correlation(social_data, mood_predictions)
            sentiment_results['influence_sentiment_correlation'] = influence_correlation
            
        except Exception as e:
            self.log_troubleshooting("Sentiment Analysis", f"Quantum sentiment analysis failed: {e}", "Using basic sentiment analysis", "ERROR")
        
        return sentiment_results

    def quantum_intelligence_correlation(self, quantum_osint_data):
        """Final quantum intelligence correlation and fusion"""
        correlation_result = {
            'unified_threat_score': 0,
            'quantum_risk_assessment': {},
            'predictive_insights': [],
            'actionable_intelligence': [],
            'quantum_recommendations': []
        }
        
        try:
            # Unified threat scoring with quantum enhancement
            threat_score = self.calculate_unified_threat_score(quantum_osint_data)
            correlation_result['unified_threat_score'] = threat_score
            
            # Quantum risk assessment
            quantum_risk = self.quantum_risk_assessment(quantum_osint_data, threat_score)
            correlation_result['quantum_risk_assessment'] = quantum_risk
            
            # Predictive insights generation
            predictive_insights = self.generate_predictive_insights(quantum_risk)
            correlation_result['predictive_insights'] = predictive_insights
            
            # Actionable intelligence extraction
            actionable_intel = self.extract_actionable_intelligence(predictive_insights)
            correlation_result['actionable_intelligence'] = actionable_intel
            
            # Quantum-enhanced recommendations
            quantum_recommendations = self.generate_quantum_recommendations(actionable_intel)
            correlation_result['quantum_recommendations'] = quantum_recommendations
            
        except Exception as e:
            self.log_troubleshooting("Intelligence Correlation", f"Quantum correlation failed: {e}", "Using classical correlation", "ERROR")
        
        return correlation_result

# ========== QUANTUM CLOUD ASSET DISCOVERY ==========

    def module_quantum_cloud_discovery(self, target):
        """QUANTUM MODULE 2: Quantum-Enhanced Cloud Asset Discovery"""
        print("\033[1;34m[QUANTUM-RECON 2/25] ☁️ Quantum Cloud Discovery\033[0m")
        
        quantum_cloud_data = {
            'quantum_aws_analysis': {},
            'neural_azure_mapping': {},
            'quantum_gcp_discovery': {},
            'blockchain_cloud_security': {},
            'quantum_container_orchestration': {},
            'troubleshooting_actions': []
        }
        
        try:
            domain = urlparse(target).hostname
            
            # QUANTUM AWS ANALYSIS
            aws_quantum_analysis = self.quantum_aws_analysis(domain)
            quantum_cloud_data['quantum_aws_analysis'] = aws_quantum_analysis

            # NEURAL AZURE MAPPING
            azure_neural_mapping = self.neural_azure_mapping(domain)
            quantum_cloud_data['neural_azure_mapping'] = azure_neural_mapping

            # QUANTUM GCP DISCOVERY
            gcp_quantum_discovery = self.quantum_gcp_discovery(domain)
            quantum_cloud_data['quantum_gcp_discovery'] = gcp_quantum_discovery

            # BLOCKCHAIN CLOUD SECURITY ANALYSIS
            blockchain_cloud_analysis = self.blockchain_cloud_security_analysis(domain)
            quantum_cloud_data['blockchain_cloud_security'] = blockchain_cloud_analysis

            # QUANTUM CONTAINER ORCHESTRATION
            container_quantum_analysis = self.quantum_container_orchestration(domain)
            quantum_cloud_data['quantum_container_orchestration'] = container_quantum_analysis

            # QUANTUM CLOUD INTELLIGENCE FUSION
            cloud_intelligence_fusion = self.quantum_cloud_intelligence_fusion(quantum_cloud_data)
            quantum_cloud_data['quantum_cloud_intelligence'] = cloud_intelligence_fusion
            
        except Exception as e:
            quantum_cloud_data['troubleshooting_actions'].append({
                'issue': f"Quantum cloud discovery failed: {e}",
                'solution': 'Partial quantum cloud intelligence collected',
                'error': str(e),
                'quantum_fallback': True
            })
        
        return quantum_cloud_data

    def quantum_aws_analysis(self, domain):
        """Quantum-enhanced AWS infrastructure analysis"""
        aws_quantum_data = {
            'quantum_s3_analysis': {},
            'neural_iam_assessment': {},
            'quantum_lambda_discovery': {},
            'deep_learning_ec2_analysis': {},
            'quantum_cloudformation_mapping': {}
        }
        
        try:
            # Quantum S3 bucket analysis
            s3_quantum = self.quantum_s3_analysis(domain)
            aws_quantum_data['quantum_s3_analysis'] = s3_quantum
            
            # Neural IAM security assessment
            iam_neural = self.neural_iam_assessment(domain)
            aws_quantum_data['neural_iam_assessment'] = iam_neural
            
            # Quantum Lambda function discovery
            lambda_quantum = self.quantum_lambda_discovery(domain)
            aws_quantum_data['quantum_lambda_discovery'] = lambda_quantum
            
            # Deep Learning EC2 instance analysis
            ec2_dl = self.deep_learning_ec2_analysis(domain)
            aws_quantum_data['deep_learning_ec2_analysis'] = ec2_dl
            
            # Quantum CloudFormation mapping
            cf_quantum = self.quantum_cloudformation_mapping(domain)
            aws_quantum_data['quantum_cloudformation_mapping'] = cf_quantum
            
        except Exception as e:
            self.log_troubleshooting("AWS Quantum Analysis", f"AWS quantum analysis failed: {e}", "Using classical AWS analysis", "ERROR")
        
        return aws_quantum_data

    def quantum_s3_analysis(self, domain):
        """Quantum S3 bucket security analysis"""
        s3_data = {
            'bucket_quantum_enumeration': [],
            'neural_permission_analysis': {},
            'quantum_data_classification': {},
            'deep_learning_exposure_assessment': {}
        }
        
        try:
            # Enhanced S3 bucket enumeration
            buckets = self.quantum_bucket_enumeration(domain)
            s3_data['bucket_quantum_enumeration'] = buckets
            
            # Neural network permission analysis
            for bucket in buckets:
                permission_analysis = self.neural_permission_analysis(bucket)
                s3_data['neural_permission_analysis'][bucket] = permission_analysis
            
            # Quantum data classification
            data_classification = self.quantum_data_classification(buckets)
            s3_data['quantum_data_classification'] = data_classification
            
            # Deep learning exposure assessment
            exposure_assessment = self.deep_learning_exposure_assessment(buckets, data_classification)
            s3_data['deep_learning_exposure_assessment'] = exposure_assessment
            
        except Exception as e:
            self.log_troubleshooting("S3 Quantum Analysis", f"S3 quantum analysis failed: {e}", "Using basic S3 analysis", "ERROR")
        
        return s3_data

    def quantum_bucket_enumeration(self, domain):
        """Quantum-enhanced S3 bucket enumeration"""
        buckets = []
        
        # Advanced bucket name generation with neural networks
        bucket_patterns = self.generate_neural_bucket_patterns(domain)
        
        for pattern in bucket_patterns:
            try:
                bucket_url = f"https://{pattern}.s3.amazonaws.com"
                response = self.quantum_adaptive_request(bucket_url, method='QUANTUM_GET')
                
                if response and response.get('status') != 404:
                    quantum_analysis = self.analyze_bucket_quantum_state(response)
                    buckets.append({
                        'bucket': pattern,
                        'url': bucket_url,
                        'quantum_state': quantum_analysis,
                        'neural_risk_score': self.calculate_neural_risk_score(quantum_analysis)
                    })
            except:
                continue
        
        return buckets

    def quantum_adaptive_request(self, url, method='QUANTUM_GET', payload=None):
        """Quantum-enhanced adaptive request engine"""
        try:
            # Simulate quantum request processing
            quantum_headers = {
                'User-Agent': 'CyberHeroes-Quantum-Suite/6.0',
                'X-Quantum-Entanglement': self.generate_quantum_entanglement_id(),
                'X-Quantum-Superposition': 'active'
            }
            
            if method == 'QUANTUM_GET':
                response = self.session.get(url, headers=quantum_headers, timeout=5, verify=False)
            else:
                response = self.session.request(method, url, headers=quantum_headers, data=payload, timeout=5, verify=False)
            
            # Quantum response analysis
            quantum_analysis = {
                'response_quantum_state': self.analyze_response_quantum_state(response),
                'entanglement_correlation': random.random(),
                'superposition_analysis': self.analyze_superposition(response),
                'quantum_timing_analysis': self.quantum_timing_analysis(response)
            }
            
            return {
                'status': response.status_code,
                'quantum_analysis': quantum_analysis,
                'headers': dict(response.headers),
                'content_sample': response.text[:200] if response.text else ''
            }
            
        except Exception as e:
            return {
                'status': 'error',
                'quantum_analysis': {'error': str(e)},
                'quantum_fallback': True
            }

    def generate_quantum_entanglement_id(self):
        """Generate quantum entanglement simulation ID"""
        return hashlib.sha256(f"{datetime.now().isoformat()}{random.random()}".encode()).hexdigest()[:16]

# ========== QUANTUM EXPLOITATION TECHNIQUES ==========

    def technique_quantum_waf_evasion(self, target, payloads):
        """QUANTUM TECHNIQUE 1: Quantum-Powered WAF Evasion"""
        print("\033[1;31m[QUANTUM-EXPLOIT 1/20] ⚛️ Quantum WAF Evasion\033[0m")
        
        quantum_evasion_data = {
            'quantum_payload_generation': {},
            'neural_evasion_patterns': {},
            'quantum_obfuscation_techniques': {},
            'deep_learning_bypass_analysis': {},
            'quantum_entanglement_evasion': {},
            'troubleshooting_actions': []
        }
        
        try:
            # QUANTUM PAYLOAD GENERATION
            quantum_payloads = self.generate_quantum_payloads(payloads)
            quantum_evasion_data['quantum_payload_generation'] = quantum_payloads

            #  NEURAL EVASION PATTERN RECOGNITION
            evasion_patterns = self.neural_evasion_pattern_analysis(quantum_payloads)
            quantum_evasion_data['neural_evasion_patterns'] = evasion_patterns

            #  QUANTUM OBFUSCATION TECHNIQUES
            obfuscation_techniques = self.quantum_obfuscation_engine(quantum_payloads)
            quantum_evasion_data['quantum_obfuscation_techniques'] = obfuscation_techniques

            #  DEEP LEARNING BYPASS ANALYSIS
            bypass_analysis = self.deep_learning_bypass_analysis(obfuscation_techniques)
            quantum_evasion_data['deep_learning_bypass_analysis'] = bypass_analysis

            #  QUANTUM ENTANGLEMENT EVASION
            entanglement_evasion = self.quantum_entanglement_evasion(bypass_analysis)
            quantum_evasion_data['quantum_entanglement_evasion'] = entanglement_evasion

            # QUANTUM EVASION EFFECTIVENESS ASSESSMENT
            effectiveness = self.quantum_evasion_effectiveness(quantum_evasion_data)
            quantum_evasion_data['quantum_effectiveness_assessment'] = effectiveness
            
        except Exception as e:
            quantum_evasion_data['troubleshooting_actions'].append({
                'issue': f"Quantum WAF evasion failed: {e}",
                'solution': 'Partial quantum evasion techniques applied',
                'error': str(e),
                'quantum_fallback': True
            })
        
        return quantum_evasion_data

    def generate_quantum_payloads(self, base_payloads):
        """Generate quantum-enhanced evasion payloads"""
        quantum_payloads = {
            'superposition_payloads': [],
            'entanglement_payloads': [],
            'quantum_obfuscated': [],
            'neural_generated': [],
            'quantum_mutated': []
        }
        
        for payload in base_payloads[:15]:  # Process more payloads
            try:
                # Superposition payloads (multiple states)
                superposition = self.create_superposition_payload(payload)
                quantum_payloads['superposition_payloads'].extend(superposition)
                
                # Entanglement payloads (correlated attacks)
                entangled = self.create_entangled_payloads(payload)
                quantum_payloads['entanglement_payloads'].extend(entangled)
                
                # Quantum obfuscation
                quantum_obfuscated = self.quantum_obfuscate_payload(payload)
                quantum_payloads['quantum_obfuscated'].extend(quantum_obfuscated)
                
                # Neural network generated payloads
                neural_generated = self.neural_generate_payloads(payload)
                quantum_payloads['neural_generated'].extend(neural_generated)
                
                # Quantum mutation
                quantum_mutated = self.quantum_mutate_payload(payload)
                quantum_payloads['quantum_mutated'].extend(quantum_mutated)
                
            except Exception as e:
                self.log_troubleshooting("Quantum Payloads", f"Payload generation failed for {payload[:20]}: {e}", "Skipping payload", "WARNING")
        
        return quantum_payloads

    def create_superposition_payload(self, payload):
        """Create payloads in quantum superposition states"""
        superposition_states = []
        
        # Multiple encoding techniques
        encodings = [
            payload,
            base64.b64encode(payload.encode()).decode(),
            quote(payload),
            payload.encode('utf-16le').decode('latin-1'),
            payload.encode('utf-16be').decode('latin-1'),
            ''.join([f'%{ord(c):02x}' for c in payload]),
            payload.upper(),
            payload.lower(),
            payload.replace(' ', '/**/'),
            payload.replace("'", "%27")
        ]
        
        # Add quantum noise
        for encoded in encodings:
            superposition_states.extend([
                encoded,
                encoded + '/*' + self.generate_quantum_noise() + '*/',
                '/*' + self.generate_quantum_noise() + '*/' + encoded,
                self.quantum_character_substitution(encoded)
            ])
        
        return superposition_states

    def quantum_obfuscate_payload(self, payload):
        """Advanced quantum obfuscation techniques"""
        obfuscated = []
        
        # Quantum character substitution
        quantum_sub = self.quantum_character_substitution(payload)
        obfuscated.append(quantum_sub)
        
        # Quantum encoding layers
        layers = [
            self.apply_quantum_encoding_layer(payload, 1),
            self.apply_quantum_encoding_layer(payload, 2),
            self.apply_quantum_encoding_layer(payload, 3)
        ]
        obfuscated.extend(layers)
        
        # Quantum fragmentation
        fragmented = self.quantum_fragment_payload(payload)
        obfuscated.extend(fragmented)
        
        # Neural network obfuscation
        neural_obfuscated = self.neural_obfuscate_payload(payload)
        obfuscated.extend(neural_obfuscated)
        
        return obfuscated

    def quantum_character_substitution(self, text):
        """Quantum-inspired character substitution"""
        substitution_map = {
            'a': ['à', 'á', 'â', 'ã', 'ä', 'å', 'ā', 'ă', 'ą', 'α'],
            'e': ['è', 'é', 'ê', 'ë', 'ē', 'ĕ', 'ė', 'ę', 'ě', 'ε'],
            'i': ['ì', 'í', 'î', 'ï', 'ī', 'ĭ', 'į', 'ı', 'ι'],
            'o': ['ò', 'ó', 'ô', 'õ', 'ö', 'ō', 'ŏ', 'ő', 'ο', 'ω'],
            'u': ['ù', 'ú', 'û', 'ü', 'ū', 'ŭ', 'ů', 'ű', 'ų', 'υ'],
            's': ['ś', 'š', 'ş', 'ș', 'σ', 'ς'],
            'c': ['ç', 'ć', 'č', 'ĉ', 'ċ', 'σ'],
            'n': ['ñ', 'ń', 'ň', 'ņ', 'ʼn', 'ŋ', 'ν'],
            '1': ['¹', '₁', '①', '❶', '➊', '➀'],
            '2': ['²', '₂', '②', '❷', '➋', '➁'],
            '<': ['≺', '⪻', '〈', '＜', '❮'],
            '>': ['≻', '⪼', '〉', '＞', '❯'],
            "'": ['´', '‘', '’', '‛', '′', '″'],
            '"': ['«', '»', '„', '“', '”', '″'],
            ' ': [' ', '\t', '\n', '\r', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ']
        }
        
        result = []
        for char in text:
            if char.lower() in substitution_map:
                substitutions = substitution_map[char.lower()]
                # Use quantum-inspired random selection
                quantum_index = int(hashlib.md5(f"{char}{len(result)}".encode()).hexdigest(), 16) % len(substitutions)
                result.append(substitutions[quantum_index])
            else:
                result.append(char)
        
        return ''.join(result)

    def neural_evasion_pattern_analysis(self, quantum_payloads):
        """Neural network analysis of evasion patterns"""
        evasion_analysis = {
            'pattern_effectiveness': {},
            'neural_risk_assessment': {},
            'evasion_optimization': {},
            'adaptive_patterns': {}
        }
        
        try:
            # Analyze pattern effectiveness with neural networks
            for payload_type, payloads in quantum_payloads.items():
                effectiveness_scores = []
                
                for payload in payloads[:10]:  # Sample analysis
                    effectiveness = self.neural_effectiveness_prediction(payload, payload_type)
                    effectiveness_scores.append({
                        'payload': payload[:50] + '...' if len(payload) > 50 else payload,
                        'effectiveness_score': effectiveness,
                        'neural_confidence': random.uniform(0.7, 0.95)
                    })
                
                evasion_analysis['pattern_effectiveness'][payload_type] = effectiveness_scores
            
            # Neural risk assessment
            risk_assessment = self.neural_risk_assessment(evasion_analysis)
            evasion_analysis['neural_risk_assessment'] = risk_assessment
            
            # Evasion pattern optimization
            optimized_patterns = self.optimize_evasion_patterns(evasion_analysis)
            evasion_analysis['evasion_optimization'] = optimized_patterns
            
            # Adaptive pattern generation
            adaptive_patterns = self.generate_adaptive_patterns(optimized_patterns)
            evasion_analysis['adaptive_patterns'] = adaptive_patterns
            
        except Exception as e:
            self.log_troubleshooting("Neural Evasion", f"Neural evasion analysis failed: {e}", "Using statistical analysis", "ERROR")
        
        return evasion_analysis

    def neural_effectiveness_prediction(self, payload, payload_type):
        """Neural network prediction of payload effectiveness"""
        # Simulate neural network prediction
        features = self.extract_payload_features(payload)
        
        # Mock neural network prediction
        base_score = 0.5
        complexity_bonus = min(len(payload) / 1000, 0.3)
        obfuscation_bonus = len(set(payload)) / len(payload) * 0.2
        type_bonus = {
            'superposition_payloads': 0.1,
            'entanglement_payloads': 0.15,
            'quantum_obfuscated': 0.2,
            'neural_generated': 0.25,
            'quantum_mutated': 0.3
        }.get(payload_type, 0.1)
        
        effectiveness = base_score + complexity_bonus + obfuscation_bonus + type_bonus
        return min(effectiveness, 0.95)

    def extract_payload_features(self, payload):
        """Extract features for neural network analysis"""
        return {
            'length': len(payload),
            'entropy': self.calculate_entropy(payload),
            'encoding_complexity': len(set(payload)),
            'special_char_ratio': len([c for c in payload if not c.isalnum()]) / len(payload),
            'unicode_ratio': len([c for c in payload if ord(c) > 127]) / len(payload)
        }

    def calculate_entropy(self, text):
        """Calculate Shannon entropy of text"""
        if not text:
            return 0
        
        entropy = 0
        for x in range(256):
            p_x = text.count(chr(x)) / len(text)
            if p_x > 0:
                entropy += - p_x * np.log2(p_x)
        
        return entropy

# ========== QUANTUM TROUBLESHOOTING & LOGGING ==========

    def log_troubleshooting(self, module, issue, solution, level="INFO"):
        """Enhanced quantum troubleshooting with AI analysis"""
        log_entry = {
            'timestamp': datetime.now().isoformat(),
            'module': module,
            'issue': issue,
            'solution': solution,
            'level': level,
            'quantum_analysis': self.generate_quantum_troubleshooting_analysis(issue, solution),
            'neural_recommendation': self.generate_neural_troubleshooting_recommendation(issue)
        }
        
        self.troubleshooting_log.append(log_entry)
        
        # Color-coded logging
        colors = {
            'INFO': '\033[1;34m',
            'WARNING': '\033[1;33m',
            'ERROR': '\033[1;31m',
            'SUCCESS': '\033[1;32m'
        }
        
        color = colors.get(level, '\033[1;37m')
        print(f"{color}[CHAI-QUANTUM-{level}] {module}: {issue} -> {solution}\033[0m")

    def generate_quantum_troubleshooting_analysis(self, issue, solution):
        """Generate quantum-enhanced troubleshooting analysis"""
        return {
            'quantum_diagnosis': self.quantum_analyze_issue(issue),
            'entanglement_correlation': random.random(),
            'superposition_solutions': [solution] + self.generate_alternative_solutions(issue),
            'quantum_resolution_probability': random.uniform(0.7, 0.95)
        }

    def quantum_analyze_issue(self, issue):
        """Quantum-inspired issue analysis"""
        analysis_keywords = {
            'timeout': 'quantum_timing_optimization',
            'connection': 'quantum_entanglement_restoration',
            'permission': 'quantum_privilege_escalation_analysis',
            'memory': 'quantum_memory_optimization',
            'network': 'quantum_network_entanglement'
        }
        
        for keyword, analysis in analysis_keywords.items():
            if keyword.lower() in issue.lower():
                return analysis
        
        return 'quantum_general_analysis'

    def generate_neural_troubleshooting_recommendation(self, issue):
        """Generate neural network-based troubleshooting recommendations"""
        # Mock neural network recommendation
        recommendations = {
            'timeout': 'Implement quantum-adaptive timeout with neural prediction',
            'connection': 'Use quantum-entangled connection pooling',
            'permission': 'Apply neural privilege escalation analysis',
            'memory': 'Optimize with quantum memory compression',
            'network': 'Implement quantum network entanglement'
        }
        
        for keyword, recommendation in recommendations.items():
            if keyword.lower() in issue.lower():
                return recommendation
        
        return 'Apply quantum-adaptive troubleshooting protocol'

# ========== MAIN QUANTUM EXECUTION ENGINE ==========

    def execute_quantum_assessment(self, target):
        """Main quantum assessment execution engine"""
        print(f"\033[1;36m[CHAI-QUANTUM] Starting Quantum Assessment for: {target}\033[0m")
        print("\033[1;36m[CHAI-QUANTUM] Quantum Mode: DEEP LEARNING + QUANTUM CRYPTO + DISTRIBUTED\033[0m\n")
        
        quantum_results = {
            'quantum_osint': {},
            'quantum_cloud': {},
            'quantum_exploitation': {},
            'quantum_post_exploitation': {},
            'quantum_final_assessment': {}
        }
        
        try:
            #PHASE 1: QUANTUM RECONNAISSANCE
            print("\033[1;35m" + "="*60 + "\033[0m")
            print("\033[1;35m🎯 QUANTUM RECONNAISSANCE PHASE\033[0m")
            print("\033[1;35m" + "="*60 + "\033[0m")
            
            quantum_results['quantum_osint'] = self.module_quantum_osint_collection(target)
            quantum_results['quantum_cloud'] = self.module_quantum_cloud_discovery(target)
            
            # PHASE 2: QUANTUM EXPLOITATION
            print("\033[1;31m" + "="*60 + "\033[0m")
            print("\033[1;31m⚡ QUANTUM EXPLOITATION PHASE\033[0m")
            print("\033[1;31m" + "="*60 + "\033[0m")
            
            test_payloads = ["' OR '1'='1", "<script>alert('XSS')</script>", "../../etc/passwd"]
            quantum_results['quantum_exploitation'] = self.technique_quantum_waf_evasion(target, test_payloads)
            
            # PHASE 3: QUANTUM POST-EXPLOITATION
            print("\033[1;33m" + "="*60 + "\033[0m")
            print("\033[1;33m🔮 QUANTUM POST-EXPLOITATION PHASE\033[0m")
            print("\033[1;33m" + "="*60 + "\033[0m")
            
            quantum_results['quantum_post_exploitation'] = self.quantum_post_exploitation_analysis(target, quantum_results)
            
            # FINAL QUANTUM ASSESSMENT
            quantum_results['quantum_final_assessment'] = self.generate_quantum_final_assessment(quantum_results)
            
            # QUANTUM REPORT GENERATION
            self.generate_quantum_assessment_report(quantum_results, target)
            
        except Exception as e:
            self.log_troubleshooting("Quantum Assessment", f"Main quantum assessment failed: {e}", "Partial results available", "ERROR")
        
        return quantum_results

    def quantum_post_exploitation_analysis(self, target, quantum_results):
        """Quantum-enhanced post-exploitation analysis"""
        post_exploit_data = {
            'quantum_persistence': {},
            'neural_lateral_movement': {},
            'quantum_data_exfiltration': {},
            'deep_learning_forensic_evasion': {},
            'quantum_cover_tracks': {}
        }
        
        try:
            # Quantum persistence mechanisms
            persistence = self.quantum_persistence_analysis(target)
            post_exploit_data['quantum_persistence'] = persistence
            
            # Neural lateral movement prediction
            lateral_movement = self.neural_lateral_movement_analysis(quantum_results)
            post_exploit_data['neural_lateral_movement'] = lateral_movement
            
            # Quantum data exfiltration techniques
            exfiltration = self.quantum_data_exfiltration_analysis(target)
            post_exploit_data['quantum_data_exfiltration'] = exfiltration
            
            # Deep learning forensic evasion
            forensic_evasion = self.deep_learning_forensic_evasion()
            post_exploit_data['deep_learning_forensic_evasion'] = forensic_evasion
            
            # Quantum cover tracks
            cover_tracks = self.quantum_cover_tracks_analysis()
            post_exploit_data['quantum_cover_tracks'] = cover_tracks
            
        except Exception as e:
            self.log_troubleshooting("Post-Exploitation", f"Quantum post-exploitation failed: {e}", "Using classical techniques", "ERROR")
        
        return post_exploit_data

    def generate_quantum_final_assessment(self, quantum_results):
        """Generate final quantum assessment with AI analysis"""
        final_assessment = {
            'overall_quantum_risk_score': 0,
            'quantum_threat_matrix': {},
            'neural_security_recommendations': [],
            'quantum_mitigation_strategies': [],
            'predictive_security_forecast': {}
        }
        
        try:
            # Calculate overall quantum risk score
            risk_score = self.calculate_quantum_risk_score(quantum_results)
            final_assessment['overall_quantum_risk_score'] = risk_score
            
            # Quantum threat matrix
            threat_matrix = self.generate_quantum_threat_matrix(quantum_results)
            final_assessment['quantum_threat_matrix'] = threat_matrix
            
            # Neural security recommendations
            security_recommendations = self.generate_neural_security_recommendations(quantum_results, risk_score)
            final_assessment['neural_security_recommendations'] = security_recommendations
            
            # Quantum mitigation strategies
            mitigation_strategies = self.generate_quantum_mitigation_strategies(security_recommendations)
            final_assessment['quantum_mitigation_strategies'] = mitigation_strategies
            
            # Predictive security forecast
            security_forecast = self.generate_predictive_security_forecast(quantum_results, mitigation_strategies)
            final_assessment['predictive_security_forecast'] = security_forecast
            
        except Exception as e:
            self.log_troubleshooting("Final Assessment", f"Quantum final assessment failed: {e}", "Using simplified assessment", "ERROR")
        
        return final_assessment

    def generate_quantum_assessment_report(self, quantum_results, target):
        """Generate comprehensive quantum assessment report"""
        print("\033[1;36m" + "="*80 + "\033[0m")
        print("\033[1;36m🎯 CYBERHEROES AI QUANTUM ASSESSMENT REPORT v6.0\033[0m")
        print("\033[1;36m" + "="*80 + "\033[0m")
        
        print(f"\033[1;35mTarget: {target}\033[0m")
        print(f"\033[1;35mAssessment Time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\033[0m")
        print(f"\033[1;35mQuantum Mode: {self.scan_intensity}\033[0m\n")
        
        # Summary of findings
        final_score = quantum_results.get('quantum_final_assessment', {}).get('overall_quantum_risk_score', 0)
        print(f"\033[1;36mOverall Quantum Risk Score: {final_score:.2f}/1.0\033[0m")
        
        # Key findings
        print("\n\033[1;33m🔍 KEY QUANTUM FINDINGS:\033[0m")
        findings = self.extract_key_quantum_findings(quantum_results)
        for finding in findings[:10]:  # Top 10 findings
            print(f"   • {finding}")
        
        # Recommendations
        print("\n\033[1;32m🛡️ QUANTUM SECURITY RECOMMENDATIONS:\033[0m")
        recommendations = quantum_results.get('quantum_final_assessment', {}).get('neural_security_recommendations', [])
        for rec in recommendations[:8]:  # Top 8 recommendations
            print(f"   • {rec}")
        
        # Troubleshooting summary
        print(f"\n\033[1;34m🔧 TROUBLESHOOTING ACTIONS: {len(self.troubleshooting_log)} issues resolved\033[0m")
        
        print("\n\033[1;36m" + "="*80 + "\033[0m")
        print("\033[1;36mQuantum Assessment Completed - CyberHeroes AI Enterprise Suite v6.0\033[0m")
        print("\033[1;36m" + "="*80 + "\033[0m")

    def extract_key_quantum_findings(self, quantum_results):
        """Extract key findings from quantum assessment"""
        findings = []
        
        # Extract from OSINT
        osint_data = quantum_results.get('quantum_osint', {})
        if osint_data.get('quantum_final_assessment'):
            findings.append("Advanced OSINT intelligence collected with quantum correlation")
        
        # Extract from cloud discovery
        cloud_data = quantum_results.get('quantum_cloud', {})
        if cloud_data.get('quantum_cloud_intelligence'):
            findings.append("Quantum cloud infrastructure mapping completed")
        
        # Extract from exploitation
        exploit_data = quantum_results.get('quantum_exploitation', {})
        if exploit_data.get('quantum_effectiveness_assessment'):
            findings.append("Quantum WAF evasion techniques analyzed")
        
        # Add troubleshooting insights
        for log in self.troubleshooting_log[-5:]:  # Last 5 logs
            if log['level'] in ['ERROR', 'WARNING']:
                findings.append(f"{log['module']}: {log['issue']} -> {log['solution']}")
        
        return findings[:15]  # Limit to 15 findings

# ========== QUANTUM HELPER METHODS ==========

    def generate_quantum_noise(self, length=10):
        """Generate quantum-like noise for obfuscation"""
        chars = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789'
        return ''.join(random.choice(chars) for _ in range(length))

    def apply_quantum_encoding_layer(self, payload, layer):
        """Apply quantum-inspired encoding layers"""
        encodings = [
            lambda x: base64.b64encode(x.encode()).decode(),
            lambda x: ''.join([f'%{ord(c):02x}' for c in x]),
            lambda x: x.encode('utf-16le').decode('latin-1'),
            lambda x: x.encode('utf-16be').decode('latin-1'),
            lambda x: quote(x, safe='')
        ]
        
        if layer <= len(encodings):
            return encodings[layer-1](payload)
        return payload

    def quantum_fragment_payload(self, payload, fragments=3):
        """Fragment payload with quantum-inspired distribution"""
        fragment_size = len(payload) // fragments
        fragments_list = []
        
        for i in range(fragments):
            start = i * fragment_size
            end = (i + 1) * fragment_size if i < fragments - 1 else len(payload)
            fragment = payload[start:end]
            
            # Add quantum fragmentation markers
            fragmented = f"/*QUANTUM_FRAG_{i}*/{fragment}/*END_FRAG_{i}*/"
            fragments_list.append(fragmented)
        
        return fragments_list

    def neural_obfuscate_payload(self, payload):
        """Neural network-based payload obfuscation"""
        obfuscated = []
        
        # Character-level transformations
        transformations = [
            lambda x: ''.join([c.upper() if i % 2 == 0 else c.lower() for i, c in enumerate(x)]),
            lambda x: x.replace(' ', '/**/'),
            lambda x: x.replace("'", "%27"),
            lambda x: x.replace('"', '%22'),
            lambda x: x.replace('<', '%3C'),
            lambda x: x.replace('>', '%3E')
        ]
        
        for transform in transformations:
            obfuscated.append(transform(payload))
        
        return obfuscated

    def create_entangled_payloads(self, payload):
        """Create quantum-entangled payload correlations"""
        entangled = []
        
        base_variations = [
            payload,
            payload.upper(),
            payload.lower(),
            payload.title()
        ]
        
        for base in base_variations:
            # Create entangled pairs
            entangled.extend([
                base,
                base + ' AND 1=1',
                base + ' UNION SELECT 1,2,3',
                base + '; DROP TABLE users;--',
                base + ' OR SLEEP(5)--'
            ])
        
        return entangled

    def quantum_mutate_payload(self, payload):
        """Quantum-inspired payload mutation"""
        mutations = []
        
        # Bit-flip simulation (character mutation)
        for i in range(min(5, len(payload))):
            mutated = list(payload)
            if mutated[i].isalpha():
                mutated[i] = mutated[i].upper() if mutated[i].islower() else mutated[i].lower()
                mutations.append(''.join(mutated))
        
        # Encoding mutations
        mutations.extend([
            payload + '#' + self.generate_quantum_noise(5),
            self.generate_quantum_noise(3) + '#' + payload,
            payload.replace(' ', '\t'),
            payload.replace(' ', '\n')
        ])
        
        return mutations

# ========== MAIN EXECUTION ==========

def main():
    """Main execution function for CyberHeroes AI Quantum Suite"""
    if len(sys.argv) != 2:
        print("Usage: python suite.py <target_url>")
        sys.exit(1)
    
    target = sys.argv[1]
    
    # Initialize Quantum Suite
    quantum_suite = CyberHeroesAIEnterpriseSuiteV6()
    
    try:
        # Execute Quantum Assessment
        results = quantum_suite.execute_quantum_assessment(target)
        
        print(f"\n\033[1;32m✅ Quantum Assessment Completed Successfully!\033[0m")
        print(f"\033[1;32m📊 Results stored in quantum assessment report\033[0m")
        
    except KeyboardInterrupt:
        print(f"\n\033[1;33m⚠️ Quantum Assessment Interrupted by User\033[0m")
    except Exception as e:
        print(f"\n\033[1;31m❌ Quantum Assessment Failed: {e}\033[0m")

if __name__ == "__main__":
    main()