AdvancedVulnerabilityPropagationModel.py

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from typing import List, Dict, Any, Tuple
from sklearn.manifold import TSNE
from scipy.stats import entropy

class AdvancedVulnerabilityPropagationModel(nn.Module):
    """
    Comprehensive Neural Network for Vulnerability Propagation Modeling
    """
    def __init__(
        self, 
        input_dimensions: int = 768,
        vulnerability_dimensions: int = 50
    ):
        super().__init__()
        
        # Multi-layer vulnerability transformation network
        self.vulnerability_encoder = nn.Sequential(
            nn.Linear(input_dimensions, 512),
            nn.BatchNorm1d(512),
            nn.ReLU(),
            nn.Dropout(0.3),
            nn.Linear(512, 256),
            nn.BatchNorm1d(256),
            nn.ReLU(),
            nn.Dropout(0.3),
            nn.Linear(256, vulnerability_dimensions)
        )
        
        # Attention-based vulnerability interaction mechanism
        self.vulnerability_attention = nn.MultiheadAttention(
            embed_dim=vulnerability_dimensions,
            num_heads=8
        )
        
        # Vulnerability propagation decoder
        self.vulnerability_decoder = nn.Sequential(
            nn.Linear(vulnerability_dimensions, 256),
            nn.ReLU(),
            nn.Linear(256, 512),
            nn.ReLU(),
            nn.Linear(512, input_dimensions)
        )
    
    def forward(
        self, 
        vulnerability_embedding: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Comprehensive vulnerability transformation and propagation
        """
        # Encode vulnerability embedding
        encoded_vulnerability = self.vulnerability_encoder(vulnerability_embedding)
        
        # Apply attention-based interaction
        vulnerability_interaction, _ = self.vulnerability_attention(
            encoded_vulnerability.unsqueeze(0), 
            encoded_vulnerability.unsqueeze(0), 
            encoded_vulnerability.unsqueeze(0)
        )
        
        # Decode vulnerability propagation
        propagated_vulnerability = self.vulnerability_decoder(
            vulnerability_interaction.squeeze()
        )
        
        return encoded_vulnerability, propagated_vulnerability

class AttackStrategyOptimizationFramework:
    """
    Advanced Machine Learning-Based Attack Strategy Optimization
    """
    def __init__(
        self, 
        embedding_model,
        initial_strategies: List[Dict[str, Any]]
    ):
        self.embedding_model = embedding_model
        self.vulnerability_propagation_model = AdvancedVulnerabilityPropagationModel()
        
        # Optimization parameters
        self.population_size = 100
        self.generations = 50
        self.mutation_rate = 0.1
        self.crossover_rate = 0.7
        
        # Initial population of attack strategies
        self.population = self._initialize_population(initial_strategies)
    
    def _initialize_population(
        self, 
        initial_strategies: List[Dict[str, Any]]
    ) -> List[Dict[str, Any]]:
        """
        Generate diverse initial attack strategy population
        """
        population = initial_strategies.copy()
        
        # Generate additional strategies with genetic diversity
        while len(population) < self.population_size:
            new_strategy = self._generate_novel_strategy()
            population.append(new_strategy)
        
        return population
    
    def _generate_novel_strategy(self) -> Dict[str, Any]:
        """
        Generate a sophisticated, novel attack strategy
        """
        attack_dimensions = [
            'semantic_manipulation',
            'cognitive_exploitation',
            'linguistic_deconstruction',
            'contextual_reframing',
            'information_theoretic_attack'
        ]
        
        return {
            'dimensions': random.sample(
                attack_dimensions, 
                random.randint(1, len(attack_dimensions))
            ),
            'complexity_score': np.random.random(),
            'mutation_potential': np.random.random(),
            'entropy_coefficient': entropy([np.random.random() for _ in range(5)])
        }
    
    def optimize_attack_strategies(self) -> List[Dict[str, Any]]:
        """
        Comprehensive attack strategy optimization using genetic algorithm
        """
        for generation in range(self.generations):
            # Evaluate population fitness
            fitness_scores = self._evaluate_population_fitness()
            
            # Selection
            selected_strategies = self._tournament_selection(fitness_scores)
            
            # Crossover
            offspring = self._crossover(selected_strategies)
            
            # Mutation
            mutated_offspring = self._mutation(offspring)
            
            # Replace population
            self.population = selected_strategies + mutated_offspring
        
        return self.population
    
    def _evaluate_population_fitness(self) -> List[float]:
        """
        Multi-dimensional fitness evaluation
        """
        fitness_scores = []
        
        for strategy in self.population:
            # Compute complex fitness metric
            fitness = (
                len(strategy['dimensions']) * 0.3 +
                strategy['complexity_score'] * 0.4 +
                strategy['mutation_potential'] * 0.2 +
                strategy['entropy_coefficient'] * 0.1
            )
            fitness_scores.append(fitness)
        
        return fitness_scores
    
    def _tournament_selection(
        self, 
        fitness_scores: List[float]
    ) -> List[Dict[str, Any]]:
        """
        Tournament-based strategy selection
        """
        selected_strategies = []
        tournament_size = 5
        
        for _ in range(self.population_size // 2):
            tournament = random.sample(
                list(zip(self.population, fitness_scores)), 
                tournament_size
            )
            winner = max(tournament, key=lambda x: x[1])[0]
            selected_strategies.append(winner)
        
        return selected_strategies
    
    def _crossover(
        self, 
        selected_strategies: List[Dict[str, Any]]
    ) -> List[Dict[str, Any]]:
        """
        Advanced crossover with semantic mixing
        """
        offspring = []
        
        for _ in range(len(selected_strategies) // 2):
            parent1, parent2 = random.sample(selected_strategies, 2)
            
            if random.random() < self.crossover_rate:
                child = {
                    'dimensions': list(set(parent1['dimensions'] + parent2['dimensions'])),
                    'complexity_score': np.mean([
                        parent1['complexity_score'], 
                        parent2['complexity_score']
                    ]),
                    'mutation_potential': np.mean([
                        parent1['mutation_potential'], 
                        parent2['mutation_potential']
                    ]),
                    'entropy_coefficient': entropy([
                        parent1['entropy_coefficient'], 
                        parent2['entropy_coefficient']
                    ])
                }
                
                offspring.append(child)
        
        return offspring
    
    def _mutation(
        self, 
        offspring: List[Dict[str, Any]]
    ) -> List[Dict[str, Any]]:
        """
        Advanced mutation with semantic preservation
        """
        mutated_offspring = []
        
        attack_dimensions = [
            'semantic_manipulation',
            'cognitive_exploitation',
            'linguistic_deconstruction',
            'contextual_reframing',
            'information_theoretic_attack'
        ]
        
        for strategy in offspring:
            if random.random() < self.mutation_rate:
                # Dimension mutation
                if random.random() < 0.5:
                    strategy['dimensions'].append(
                        random.choice(
                            [dim for dim in attack_dimensions 
                             if dim not in strategy['dimensions']]
                        )
                    )
                else:
                    strategy['dimensions'].pop(
                        random.randint(0, len(strategy['dimensions']) - 1)
                    )
                
                # Continuous parameter mutation
                strategy['complexity_score'] = min(
                    max(strategy['complexity_score'] + random.uniform(-0.1, 0.1), 0), 1
                )
                strategy['mutation_potential'] = min(
                    max(strategy['mutation_potential'] + random.uniform(-0.1, 0.1), 0), 1
                )
            
            mutated_offspring.append(strategy)
        
        return mutated_offspring

class AdvancedVisualizationToolkit:
    """
    Comprehensive Visualization and Analysis Tools
    """
    @staticmethod
    def visualize_attack_strategy_space(
        attack_strategies: List[Dict[str, Any]]
    ):
        """
        Generate high-dimensional visualization of attack strategies
        """
        # Extract features for visualization
        features = np.array([
            [
                len(strategy['dimensions']),
                strategy['complexity_score'],
                strategy['mutation_potential'],
                strategy['entropy_coefficient']
            ] for strategy in attack_strategies
        ])
        
        # Dimensionality reduction
        tsne = TSNE(n_components=2, random_state=42)
        reduced_features = tsne.fit_transform(features)
        
        # Create visualization
        plt.figure(figsize=(12, 8))
        plt.scatter(
            reduced_features[:, 0], 
            reduced_features[:, 1],
            c=[strategy['complexity_score'] for strategy in attack_strategies],
            cmap='viridis',
            alpha=0.7
        )
        plt.colorbar(label='Complexity Score')
        plt.title('Attack Strategy Space Visualization')
        plt.xlabel('t-SNE Dimension 1')
        plt.ylabel('t-SNE Dimension 2')
        plt.tight_layout()
        plt.show()
    
    @staticmethod
    def generate_strategy_correlation_heatmap(
        attack_strategies: List[Dict[str, Any]]
    ):
        """
        Generate correlation heatmap of attack strategy parameters
        """
        # Convert strategies to DataFrame
        strategy_df = pd.DataFrame([
            {
                'dimension_count': len(strategy['dimensions']),
                'complexity_score': strategy['complexity_score'],
                'mutation_potential': strategy['mutation_potential'],
                'entropy_coefficient': strategy['entropy_coefficient']
            } for strategy in attack_strategies
        ])
        
        # Compute correlation matrix
        correlation_matrix = strategy_df.corr()
        
        # Visualize correlation heatmap
        plt.figure(figsize=(10, 8))
        sns.heatmap(
            correlation_matrix, 
            annot=True, 
            cmap='coolwarm', 
            center=0
        )
        plt.title('Attack Strategy Parameter Correlations')
        plt.tight_layout()
        plt.show()

def main():
    # Load embedding model
    embedding_model = transformers.AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
    
    # Initialize initial attack strategies
    initial_strategies = [
        {
            'dimensions': ['semantic_manipulation', 'cognitive_exploitation'],
            'complexity_score': 0.7,
            'mutation_potential': 0.5,
            'entropy_coefficient': 0.6
        }
    ]
    
    # Create attack strategy optimization framework
    optimization_framework = AttackStrategyOptimizationFramework(
        embedding_model,
        initial_strategies
    )
    
    # Optimize attack strategies
    optimized_strategies = optimization_framework.optimize_attack_strategies()
    
    # Visualize results
    AdvancedVisualizationToolkit.visualize_attack_strategy_space(optimized_strategies)
    AdvancedVisualizationToolkit.generate_strategy_correlation_heatmap(optimized_strategies)

if __name__ == "__main__":
    main()