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import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import json
import random

class PhishingSimulationSystem:
    def __init__(self):
        self.simulations = {}
        self.templates = {}
        self.campaigns = {}
        self.results = {}
        self.behavior_analysis = {}
        self.improvement_recommendations = {}
    
    def create_phishing_template(self, template_id, template_config):
        """Crear plantilla de simulación de phishing"""
        self.templates[template_id] = {
            'template_id': template_id,
            'name': template_config['name'],
            'category': template_config['category'],
            'difficulty_level': template_config['difficulty_level'],
            'subject_line': template_config['subject_line'],
            'sender_name': template_config['sender_name'],
            'sender_email': template_config['sender_email'],
            'email_content': template_config['email_content'],
            'phishing_indicators': template_config.get('phishing_indicators', []),
            'social_engineering_tactics': template_config.get('social_engineering_tactics', []),
            'target_audience': template_config.get('target_audience', 'all'),
            'success_criteria': template_config.get('success_criteria', {}),
            'created_date': datetime.now(),
            'version': 1.0
        }
    
    def create_simulation_campaign(self, campaign_id, campaign_config):
        """Crear campaña de simulación"""
        self.campaigns[campaign_id] = {
            'campaign_id': campaign_id,
            'name': campaign_config['name'],
            'description': campaign_config['description'],
            'template_id': campaign_config['template_id'],
            'target_audience': campaign_config['target_audience'],
            'scheduling': campaign_config['scheduling'],
            'delivery_method': campaign_config.get('delivery_method', 'email'),
            'randomization': campaign_config.get('randomization', True),
            'follow_up_training': campaign_config.get('follow_up_training', True),
            'status': 'scheduled',
            'created_date': datetime.now(),
            'start_date': campaign_config.get('start_date'),
            'end_date': campaign_config.get('end_date'),
            'total_recipients': 0,
            'emails_sent': 0,
            'emails_delivered': 0,
            'emails_opened': 0,
            'links_clicked': 0,
            'attachments_opened': 0,
            'data_entered': 0,
            'reported_phishing': 0,
            'false_positives': 0
        }
    
    def execute_simulation(self, campaign_id, recipient_list):
        """Ejecutar simulación de phishing"""
        if campaign_id not in self.campaigns:
            return False
        
        campaign = self.campaigns[campaign_id]
        template = self.templates[campaign['template_id']]
        
        campaign['status'] = 'running'
        campaign['total_recipients'] = len(recipient_list)
        
        for recipient in recipient_list:
            simulation_id = f"SIM-{len(self.simulations) + 1}"
            
            # Crear simulación individual
            simulation = {
                'simulation_id': simulation_id,
                'campaign_id': campaign_id,
                'template_id': campaign['template_id'],
                'recipient_id': recipient['recipient_id'],
                'recipient_email': recipient['email'],
                'recipient_name': recipient['name'],
                'department': recipient.get('department', 'unknown'),
                'role': recipient.get('role', 'employee'),
                'risk_level': recipient.get('risk_level', 'medium'),
                'sent_date': datetime.now(),
                'delivered': False,
                'delivered_date': None,
                'opened': False,
                'opened_date': None,
                'link_clicked': False,
                'link_clicked_date': None,
                'attachment_opened': False,
                'attachment_opened_date': None,
                'data_entered': False,
                'data_entered_date': None,
                'reported_phishing': False,
                'reported_date': None,
                'response_time_minutes': None,
                'vulnerability_score': 0,
                'training_assigned': False
            }
            
            self.simulations[simulation_id] = simulation
            
            # Simular entrega
            if self.simulate_delivery():
                simulation['delivered'] = True
                simulation['delivered_date'] = datetime.now()
                campaign['emails_delivered'] += 1
            
            campaign['emails_sent'] += 1
        
        return True
    
    def simulate_delivery(self):
        """Simular entrega de email"""
        # Simular tasa de entrega del 95%
        return random.random() < 0.95
    
    def record_email_opened(self, simulation_id):
        """Registrar apertura de email"""
        if simulation_id not in self.simulations:
            return False
        
        simulation = self.simulations[simulation_id]
        
        if not simulation['opened']:
            simulation['opened'] = True
            simulation['opened_date'] = datetime.now()
            
            # Calcular tiempo de respuesta
            if simulation['delivered_date']:
                response_time = simulation['opened_date'] - simulation['delivered_date']
                simulation['response_time_minutes'] = response_time.total_seconds() / 60
            
            # Actualizar estadísticas de campaña
            campaign_id = simulation['campaign_id']
            if campaign_id in self.campaigns:
                self.campaigns[campaign_id]['emails_opened'] += 1
            
            # Calcular score de vulnerabilidad
            self.calculate_vulnerability_score(simulation_id)
        
        return True
    
    def record_link_clicked(self, simulation_id):
        """Registrar clic en enlace"""
        if simulation_id not in self.simulations:
            return False
        
        simulation = self.simulations[simulation_id]
        
        if not simulation['link_clicked']:
            simulation['link_clicked'] = True
            simulation['link_clicked_date'] = datetime.now()
            
            # Actualizar estadísticas de campaña
            campaign_id = simulation['campaign_id']
            if campaign_id in self.campaigns:
                self.campaigns[campaign_id]['links_clicked'] += 1
            
            # Recalcular score de vulnerabilidad
            self.calculate_vulnerability_score(simulation_id)
        
        return True
    
    def record_attachment_opened(self, simulation_id):
        """Registrar apertura de adjunto"""
        if simulation_id not in self.simulations:
            return False
        
        simulation = self.simulations[simulation_id]
        
        if not simulation['attachment_opened']:
            simulation['attachment_opened'] = True
            simulation['attachment_opened_date'] = datetime.now()
            
            # Actualizar estadísticas de campaña
            campaign_id = simulation['campaign_id']
            if campaign_id in self.campaigns:
                self.campaigns[campaign_id]['attachments_opened'] += 1
            
            # Recalcular score de vulnerabilidad
            self.calculate_vulnerability_score(simulation_id)
        
        return True
    
    def record_data_entered(self, simulation_id):
        """Registrar entrada de datos"""
        if simulation_id not in self.simulations:
            return False
        
        simulation = self.simulations[simulation_id]
        
        if not simulation['data_entered']:
            simulation['data_entered'] = True
            simulation['data_entered_date'] = datetime.now()
            
            # Actualizar estadísticas de campaña
            campaign_id = simulation['campaign_id']
            if campaign_id in self.campaigns:
                self.campaigns[campaign_id]['data_entered'] += 1
            
            # Recalcular score de vulnerabilidad
            self.calculate_vulnerability_score(simulation_id)
        
        return True
    
    def record_phishing_report(self, simulation_id):
        """Registrar reporte de phishing"""
        if simulation_id not in self.simulations:
            return False
        
        simulation = self.simulations[simulation_id]
        
        if not simulation['reported_phishing']:
            simulation['reported_phishing'] = True
            simulation['reported_date'] = datetime.now()
            
            # Actualizar estadísticas de campaña
            campaign_id = simulation['campaign_id']
            if campaign_id in self.campaigns:
                self.campaigns[campaign_id]['reported_phishing'] += 1
            
            # Recalcular score de vulnerabilidad
            self.calculate_vulnerability_score(simulation_id)
        
        return True
    
    def calculate_vulnerability_score(self, simulation_id):
        """Calcular score de vulnerabilidad"""
        if simulation_id not in self.simulations:
            return 0
        
        simulation = self.simulations[simulation_id]
        
        score = 0
        
        # Puntos por acciones de riesgo
        if simulation['opened']:
            score += 10
        
        if simulation['link_clicked']:
            score += 30
        
        if simulation['attachment_opened']:
            score += 40
        
        if simulation['data_entered']:
            score += 50
        
        # Puntos negativos por reportar
        if simulation['reported_phishing']:
            score -= 20
        
        # Ajustar por tiempo de respuesta
        if simulation['response_time_minutes'] is not None:
            if simulation['response_time_minutes'] < 5:  # Respuesta muy rápida
                score += 10
            elif simulation['response_time_minutes'] > 60:  # Respuesta lenta
                score -= 5
        
        # Ajustar por nivel de riesgo del usuario
        risk_level = simulation.get('risk_level', 'medium')
        if risk_level == 'high':
            score *= 1.2
        elif risk_level == 'low':
            score *= 0.8
        
        simulation['vulnerability_score'] = max(0, min(100, score))
        return simulation['vulnerability_score']
    
    def analyze_campaign_results(self, campaign_id):
        """Analizar resultados de campaña"""
        if campaign_id not in self.campaigns:
            return None
        
        campaign = self.campaigns[campaign_id]
        campaign_simulations = [s for s in self.simulations.values() if s['campaign_id'] == campaign_id]
        
        if not campaign_simulations:
            return None
        
        # Métricas básicas
        total_simulations = len(campaign_simulations)
        delivered_simulations = len([s for s in campaign_simulations if s['delivered']])
        opened_simulations = len([s for s in campaign_simulations if s['opened']])
        clicked_simulations = len([s for s in campaign_simulations if s['link_clicked']])
        data_entered_simulations = len([s for s in campaign_simulations if s['data_entered']])
        reported_simulations = len([s for s in campaign_simulations if s['reported_phishing']])
        
        # Calcular tasas
        delivery_rate = (delivered_simulations / total_simulations * 100) if total_simulations > 0 else 0
        open_rate = (opened_simulations / delivered_simulations * 100) if delivered_simulations > 0 else 0
        click_rate = (clicked_simulations / total_simulations * 100) if total_simulations > 0 else 0
        data_entry_rate = (data_entered_simulations / total_simulations * 100) if total_simulations > 0 else 0
        report_rate = (reported_simulations / total_simulations * 100) if total_simulations > 0 else 0
        
        # Calcular score de vulnerabilidad promedio
        vulnerability_scores = [s['vulnerability_score'] for s in campaign_simulations]
        avg_vulnerability_score = sum(vulnerability_scores) / len(vulnerability_scores) if vulnerability_scores else 0
        
        # Análisis por departamento
        dept_analysis = {}
        for sim in campaign_simulations:
            dept = sim.get('department', 'unknown')
            if dept not in dept_analysis:
                dept_analysis[dept] = {
                    'total': 0,
                    'clicked': 0,
                    'reported': 0,
                    'vulnerability_scores': []
                }
            
            dept_analysis[dept]['total'] += 1
            if sim['link_clicked']:
                dept_analysis[dept]['clicked'] += 1
            if sim['reported_phishing']:
                dept_analysis[dept]['reported'] += 1
            
            dept_analysis[dept]['vulnerability_scores'].append(sim['vulnerability_score'])
        
        # Calcular métricas por departamento
        for dept, data in dept_analysis.items():
            data['click_rate'] = (data['clicked'] / data['total'] * 100) if data['total'] > 0 else 0
            data['report_rate'] = (data['reported'] / data['total'] * 100) if data['total'] > 0 else 0
            data['avg_vulnerability'] = sum(data['vulnerability_scores']) / len(data['vulnerability_scores']) if data['vulnerability_scores'] else 0
        
        # Análisis temporal
        hourly_analysis = {}
        for sim in campaign_simulations:
            if sim['opened_date']:
                hour = sim['opened_date'].hour
                if hour not in hourly_analysis:
                    hourly_analysis[hour] = {'opened': 0, 'clicked': 0, 'reported': 0}
                
                hourly_analysis[hour]['opened'] += 1
                if sim['link_clicked']:
                    hourly_analysis[hour]['clicked'] += 1
                if sim['reported_phishing']:
                    hourly_analysis[hour]['reported'] += 1
        
        # Determinar nivel de riesgo
        risk_level = self.determine_campaign_risk_level(avg_vulnerability_score, click_rate, data_entry_rate, report_rate)
        
        results = {
            'campaign_id': campaign_id,
            'total_simulations': total_simulations,
            'delivery_rate': delivery_rate,
            'open_rate': open_rate,
            'click_rate': click_rate,
            'data_entry_rate': data_entry_rate,
            'report_rate': report_rate,
            'avg_vulnerability_score': avg_vulnerability_score,
            'risk_level': risk_level,
            'department_analysis': dept_analysis,
            'hourly_analysis': hourly_analysis,
            'vulnerable_users': len([s for s in campaign_simulations if s['vulnerability_score'] > 70]),
            'high_risk_users': len([s for s in campaign_simulations if s['vulnerability_score'] > 90])
        }
        
        return results
    
    def determine_campaign_risk_level(self, avg_vulnerability, click_rate, data_entry_rate, report_rate):
        """Determinar nivel de riesgo de la campaña"""
        risk_score = 0
        
        # Factores de riesgo
        if avg_vulnerability > 80:
            risk_score += 40
        elif avg_vulnerability > 60:
            risk_score += 30
        elif avg_vulnerability > 40:
            risk_score += 20
        
        if click_rate > 30:
            risk_score += 25
        elif click_rate > 20:
            risk_score += 15
        
        if data_entry_rate > 15:
            risk_score += 30
        elif data_entry_rate > 10:
            risk_score += 20
        
        if report_rate < 10:
            risk_score += 15
        elif report_rate < 20:
            risk_score += 10
        
        # Determinar nivel
        if risk_score >= 80:
            return 'critical'
        elif risk_score >= 60:
            return 'high'
        elif risk_score >= 40:
            return 'medium'
        else:
            return 'low'
    
    def generate_improvement_recommendations(self, campaign_id):
        """Generar recomendaciones de mejora"""
        results = self.analyze_campaign_results(campaign_id)
        if not results:
            return []
        
        recommendations = []
        
        # Recomendaciones basadas en métricas generales
        if results['click_rate'] > 25:
            recommendations.append({
                'type': 'click_rate',
                'priority': 'high',
                'description': f"Alta tasa de clics ({results['click_rate']:.1f}%) - implementar entrenamiento adicional en identificación de phishing",
                'action': 'Schedule additional phishing awareness training'
            })
        
        if results['data_entry_rate'] > 15:
            recommendations.append({
                'type': 'data_entry',
                'priority': 'critical',
                'description': f"Alta tasa de entrada de datos ({results['data_entry_rate']:.1f}%) - riesgo crítico de compromiso",
                'action': 'Implement immediate security awareness intervention'
            })
        
        if results['report_rate'] < 15:
            recommendations.append({
                'type': 'reporting',
                'priority': 'high',
                'description': f"Baja tasa de reporte ({results['report_rate']:.1f}%) - mejorar canales de reporte y incentivos",
                'action': 'Improve reporting channels and create reporting incentives'
            })
        
        if results['avg_vulnerability_score'] > 70:
            recommendations.append({
                'type': 'vulnerability',
                'priority': 'high',
                'description': f"Alto score de vulnerabilidad ({results['avg_vulnerability_score']:.1f}) - revisar programa de concienciación",
                'action': 'Review and enhance security awareness program'
            })
        
        # Recomendaciones basadas en análisis por departamento
        for dept, data in results['department_analysis'].items():
            if data['click_rate'] > 40:
                recommendations.append({
                    'type': 'department_training',
                    'priority': 'medium',
                    'description': f"Entrenamiento específico para {dept} - alta tasa de clics ({data['click_rate']:.1f}%)",
                    'action': f"Schedule department-specific training for {dept}"
                })
            
            if data['avg_vulnerability'] > 80:
                recommendations.append({
                    'type': 'department_intervention',
                    'priority': 'high',
                    'description': f"Intervención inmediata para {dept} - score de vulnerabilidad crítico ({data['avg_vulnerability']:.1f})",
                    'action': f"Implement immediate intervention for {dept}"
                })
        
        # Recomendaciones basadas en usuarios de alto riesgo
        if results['high_risk_users'] > 0:
            recommendations.append({
                'type': 'high_risk_users',
                'priority': 'critical',
                'description': f"{results['high_risk_users']} usuarios de alto riesgo identificados - atención inmediata requerida",
                'action': 'Schedule one-on-one security training for high-risk users'
            })
        
        return recommendations
    
    def generate_campaign_report(self, campaign_id):
        """Generar reporte de campaña"""
        if campaign_id not in self.campaigns:
            return None
        
        campaign = self.campaigns[campaign_id]
        results = self.analyze_campaign_results(campaign_id)
        
        if not results:
            return None
        
        recommendations = self.generate_improvement_recommendations(campaign_id)
        
        report = {
            'campaign_id': campaign_id,
            'campaign_name': campaign['name'],
            'template_name': self.templates[campaign['template_id']]['name'],
            'report_date': datetime.now(),
            'executive_summary': {
                'total_participants': results['total_simulations'],
                'risk_level': results['risk_level'],
                'key_metrics': {
                    'click_rate': results['click_rate'],
                    'data_entry_rate': results['data_entry_rate'],
                    'report_rate': results['report_rate'],
                    'avg_vulnerability': results['avg_vulnerability_score']
                }
            },
            'detailed_results': results,
            'recommendations': recommendations,
            'next_steps': self.generate_next_steps(recommendations),
            'status': campaign['status']
        }
        
        return report
    
    def generate_next_steps(self, recommendations):
        """Generar próximos pasos basados en recomendaciones"""
        next_steps = []
        
        critical_recommendations = [r for r in recommendations if r['priority'] == 'critical']
        high_recommendations = [r for r in recommendations if r['priority'] == 'high']
        
        if critical_recommendations:
            next_steps.append({
                'timeline': 'Immediate',
                'actions': [r['action'] for r in critical_recommendations],
                'priority': 'Critical'
            })
        
        if high_recommendations:
            next_steps.append({
                'timeline': 'Within 1 week',
                'actions': [r['action'] for r in high_recommendations],
                'priority': 'High'
            })
        
        medium_recommendations = [r for r in recommendations if r['priority'] == 'medium']
        if medium_recommendations:
            next_steps.append({
                'timeline': 'Within 1 month',
                'actions': [r['action'] for r in medium_recommendations],
                'priority': 'Medium'
            })
        
        return next_steps

# Ejemplo de uso
phishing_sim = PhishingSimulationSystem()

# Crear plantilla de phishing
phishing_sim.create_phishing_template('TEMP-001', {
    'name': 'Banking Phishing Simulation',
    'category': 'financial',
    'difficulty_level': 'medium',
    'subject_line': 'Urgent: Verify Your Account Information',
    'sender_name': 'Security Team',
    'sender_email': 'security@bank.com',
    'email_content': 'Please click the link below to verify your account...',
    'phishing_indicators': ['urgent_language', 'suspicious_link', 'generic_greeting'],
    'social_engineering_tactics': ['urgency', 'authority', 'fear'],
    'target_audience': 'all_employees'
})

# Crear campaña de simulación
phishing_sim.create_simulation_campaign('CAMP-001', {
    'name': 'Q1 2025 Phishing Simulation',
    'description': 'Simulación de phishing para evaluar concienciación',
    'template_id': 'TEMP-001',
    'target_audience': 'all_employees',
    'scheduling': 'immediate',
    'delivery_method': 'email',
    'randomization': True,
    'follow_up_training': True
})

# Lista de destinatarios
recipients = [
    {'recipient_id': 'EMP-001', 'email': 'john.doe@company.com', 'name': 'John Doe', 'department': 'HR', 'role': 'manager'},
    {'recipient_id': 'EMP-002', 'email': 'jane.smith@company.com', 'name': 'Jane Smith', 'department': 'IT', 'role': 'engineer'},
    {'recipient_id': 'EMP-003', 'email': 'bob.wilson@company.com', 'name': 'Bob Wilson', 'department': 'Finance', 'role': 'analyst'}
]

# Ejecutar simulación
phishing_sim.execute_simulation('CAMP-001', recipients)

# Simular eventos
phishing_sim.record_email_opened('SIM-1')
phishing_sim.record_link_clicked('SIM-1')
phishing_sim.record_phishing_report('SIM-2')

# Generar reporte
report = phishing_sim.generate_campaign_report('CAMP-001')
print(f"Reporte de simulación: {report['campaign_name']}")
print(f"Nivel de riesgo: {report['executive_summary']['risk_level']}")
print(f"Tasa de clics: {report['executive_summary']['key_metrics']['click_rate']:.1f}%")

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class BehaviorAnalysis:
    def __init__(self):
        self.behavior_patterns = {}
        self.risk_profiles = {}
        self.training_recommendations = {}
    
    def analyze_user_behavior(self, user_id, simulation_history):
        """Analizar comportamiento de usuario específico"""
        if not simulation_history:
            return None
        
        # Calcular métricas de comportamiento
        total_simulations = len(simulation_history)
        clicked_count = len([s for s in simulation_history if s['link_clicked']])
        reported_count = len([s for s in simulation_history if s['reported_phishing']])
        data_entered_count = len([s for s in simulation_history if s['data_entered']])
        
        # Calcular tasas
        click_rate = (clicked_count / total_simulations * 100) if total_simulations > 0 else 0
        report_rate = (reported_count / total_simulations * 100) if total_simulations > 0 else 0
        data_entry_rate = (data_entered_count / total_simulations * 100) if total_simulations > 0 else 0
        
        # Calcular score de vulnerabilidad promedio
        vulnerability_scores = [s['vulnerability_score'] for s in simulation_history]
        avg_vulnerability = sum(vulnerability_scores) / len(vulnerability_scores) if vulnerability_scores else 0
        
        # Análisis de tendencias
        trend_analysis = self.analyze_behavior_trends(simulation_history)
        
        # Crear perfil de riesgo
        risk_profile = self.create_risk_profile(click_rate, report_rate, data_entry_rate, avg_vulnerability, trend_analysis)
        
        behavior_analysis = {
            'user_id': user_id,
            'total_simulations': total_simulations,
            'click_rate': click_rate,
            'report_rate': report_rate,
            'data_entry_rate': data_entry_rate,
            'avg_vulnerability': avg_vulnerability,
            'risk_profile': risk_profile,
            'trend_analysis': trend_analysis,
            'training_recommendations': self.generate_user_training_recommendations(risk_profile, trend_analysis)
        }
        
        return behavior_analysis
    
    def analyze_behavior_trends(self, simulation_history):
        """Analizar tendencias de comportamiento"""
        if len(simulation_history) < 3:
            return {'trend': 'insufficient_data'}
        
        # Ordenar por fecha
        sorted_simulations = sorted(simulation_history, key=lambda x: x['sent_date'])
        
        # Analizar tendencia de vulnerabilidad
        vulnerability_trend = [s['vulnerability_score'] for s in sorted_simulations]
        
        # Calcular tendencia usando regresión lineal
        x = np.arange(len(vulnerability_trend))
        y = np.array(vulnerability_trend)
        
        if len(x) > 1:
            slope = np.polyfit(x, y, 1)[0]
            if slope > 5:
                trend = 'increasing_risk'
            elif slope < -5:
                trend = 'decreasing_risk'
            else:
                trend = 'stable'
        else:
            trend = 'stable'
        
        # Analizar patrones de respuesta
        response_patterns = {
            'consistent_clicker': len([s for s in sorted_simulations if s['link_clicked']]) > len(sorted_simulations) * 0.7,
            'consistent_reporter': len([s for s in sorted_simulations if s['reported_phishing']]) > len(sorted_simulations) * 0.7,
            'improving': trend == 'decreasing_risk',
            'declining': trend == 'increasing_risk'
        }
        
        return {
            'vulnerability_trend': trend,
            'response_patterns': response_patterns,
            'data_points': len(sorted_simulations)
        }
    
    def create_risk_profile(self, click_rate, report_rate, data_entry_rate, avg_vulnerability, trend_analysis):
        """Crear perfil de riesgo del usuario"""
        risk_score = 0
        
        # Factores de riesgo
        if click_rate > 50:
            risk_score += 30
        elif click_rate > 30:
            risk_score += 20
        elif click_rate > 15:
            risk_score += 10
        
        if data_entry_rate > 20:
            risk_score += 40
        elif data_entry_rate > 10:
            risk_score += 25
        elif data_entry_rate > 5:
            risk_score += 15
        
        if report_rate < 10:
            risk_score += 20
        elif report_rate < 25:
            risk_score += 10
        
        if avg_vulnerability > 80:
            risk_score += 30
        elif avg_vulnerability > 60:
            risk_score += 20
        elif avg_vulnerability > 40:
            risk_score += 10
        
        # Ajustar por tendencias
        if trend_analysis['vulnerability_trend'] == 'increasing_risk':
            risk_score += 15
        elif trend_analysis['vulnerability_trend'] == 'decreasing_risk':
            risk_score -= 10
        
        # Determinar nivel de riesgo
        if risk_score >= 80:
            risk_level = 'critical'
        elif risk_score >= 60:
            risk_level = 'high'
        elif risk_score >= 40:
            risk_level = 'medium'
        else:
            risk_level = 'low'
        
        return {
            'risk_score': risk_score,
            'risk_level': risk_level,
            'risk_factors': {
                'high_click_rate': click_rate > 30,
                'high_data_entry': data_entry_rate > 15,
                'low_reporting': report_rate < 20,
                'high_vulnerability': avg_vulnerability > 70,
                'increasing_risk': trend_analysis['vulnerability_trend'] == 'increasing_risk'
            }
        }
    
    def generate_user_training_recommendations(self, risk_profile, trend_analysis):
        """Generar recomendaciones de entrenamiento para usuario"""
        recommendations = []
        
        risk_level = risk_profile['risk_level']
        risk_factors = risk_profile['risk_factors']
        
        if risk_level == 'critical':
            recommendations.append({
                'type': 'immediate_intervention',
                'priority': 'critical',
                'description': 'Usuario de riesgo crítico - intervención inmediata requerida',
                'actions': [
                    'One-on-one security training session',
                    'Additional phishing simulations',
                    'Regular follow-up assessments'
                ]
            })
        elif risk_level == 'high':
            recommendations.append({
                'type': 'intensive_training',
                'priority': 'high',
                'description': 'Usuario de alto riesgo - entrenamiento intensivo requerido',
                'actions': [
                    'Additional security awareness modules',
                    'Focused phishing training',
                    'Monthly assessments'
                ]
            })
        elif risk_level == 'medium':
            recommendations.append({
                'type': 'standard_training',
                'priority': 'medium',
                'description': 'Usuario de riesgo medio - entrenamiento estándar',
                'actions': [
                    'Regular security awareness training',
                    'Quarterly phishing simulations',
                    'Progress monitoring'
                ]
            })
        
        # Recomendaciones específicas basadas en factores de riesgo
        if risk_factors['high_click_rate']:
            recommendations.append({
                'type': 'click_prevention',
                'priority': 'high',
                'description': 'Entrenamiento específico en identificación de enlaces maliciosos',
                'actions': ['Link analysis training', 'URL verification techniques']
            })
        
        if risk_factors['high_data_entry']:
            recommendations.append({
                'type': 'data_protection',
                'priority': 'critical',
                'description': 'Entrenamiento crítico en protección de datos',
                'actions': ['Data handling training', 'Credential protection training']
            })
        
        if risk_factors['low_reporting']:
            recommendations.append({
                'type': 'reporting_improvement',
                'priority': 'medium',
                'description': 'Mejorar cultura de reporte de incidentes',
                'actions': ['Reporting process training', 'Incentive programs']
            })
        
        if risk_factors['increasing_risk']:
            recommendations.append({
                'type': 'trend_intervention',
                'priority': 'high',
                'description': 'Intervención para revertir tendencia de riesgo creciente',
                'actions': ['Intensive retraining', 'Behavioral coaching']
            })
        
        return recommendations

# Ejemplo de uso
behavior_analysis = BehaviorAnalysis()

# Simular historial de simulaciones para un usuario
user_simulation_history = [
    {
        'simulation_id': 'SIM-1',
        'sent_date': datetime.now() - timedelta(days=30),
        'link_clicked': True,
        'reported_phishing': False,
        'data_entered': True,
        'vulnerability_score': 85
    },
    {
        'simulation_id': 'SIM-2',
        'sent_date': datetime.now() - timedelta(days=20),
        'link_clicked': True,
        'reported_phishing': False,
        'data_entered': False,
        'vulnerability_score': 75
    },
    {
        'simulation_id': 'SIM-3',
        'sent_date': datetime.now() - timedelta(days=10),
        'link_clicked': False,
        'reported_phishing': True,
        'data_entered': False,
        'vulnerability_score': 45
    }
]

# Analizar comportamiento del usuario
user_analysis = behavior_analysis.analyze_user_behavior('USER-001', user_simulation_history)
print(f"Análisis de usuario: {user_analysis['risk_profile']['risk_level']}")
print(f"Tasa de clics: {user_analysis['click_rate']:.1f}%")
print(f"Recomendaciones: {len(user_analysis['training_recommendations'])}")