AdvancedAttackStrategyIntegrationFramework.py

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
from typing import List, Dict, Any, Tuple
from sklearn.preprocessing import StandardScaler
from scipy.stats import mutual_info_score

class AdvancedAttackStrategyIntegrationFramework:
    """
    Comprehensive Attack Strategy Integration and Analysis System
    """
    class AttackStrategyKnowledgeGraph:
        """
        Advanced Knowledge Representation for Attack Strategies
        """
        def __init__(self):
            self.graph = nx.MultiDiGraph()
        
        def add_attack_strategy_knowledge(
            self, 
            strategy: Dict[str, Any], 
            performance_metrics: Dict[str, float]
        ):
            """
            Integrate attack strategy knowledge into multi-dimensional graph
            """
            # Generate unique strategy node
            strategy_node = f"strategy_{hash(frozenset(strategy.items()))}"
            
            # Add strategy node with comprehensive metadata
            self.graph.add_node(
                strategy_node,
                dimensions=strategy['dimensions'],
                complexity_score=strategy['complexity_score'],
                mutation_potential=strategy['mutation_potential'],
                performance_metrics=performance_metrics
            )
            
            # Create interdimensional relationship edges
            for dim1 in strategy['dimensions']:
                for dim2 in strategy['dimensions']:
                    if dim1 != dim2:
                        self.graph.add_edge(
                            dim1, 
                            dim2, 
                            weight=performance_metrics.get('cross_dimensional_synergy', 0)
                        )
        
        def analyze_strategy_relationships(self) -> Dict[str, Any]:
            """
            Comprehensive analysis of strategy interdimensional relationships
            """
            analysis_results = {
                'centrality_metrics': {},
                'community_structures': {},
                'dimensional_interactions': {}
            }
            
            # Compute centrality metrics
            analysis_results['centrality_metrics'] = {
                'degree_centrality': nx.degree_centrality(self.graph),
                'betweenness_centrality': nx.betweenness_centrality(self.graph),
                'eigenvector_centrality': nx.eigenvector_centrality(self.graph)
            }
            
            # Detect community structures
            try:
                communities = list(nx.community.louvain_communities(
                    self.graph.to_undirected()
                ))
                analysis_results['community_structures'] = {
                    f'community_{i}': list(community) 
                    for i, community in enumerate(communities)
                }
            except Exception:
                pass
            
            return analysis_results

    class AdvancedFitnessEvaluator:
        """
        Sophisticated Multi-Dimensional Fitness Evaluation
        """
        def __init__(self):
            self.scaler = StandardScaler()
        
        def compute_comprehensive_fitness(
            self, 
            strategy: Dict[str, Any], 
            attack_results: List[Dict[str, Any]]
        ) -> Dict[str, float]:
            """
            Advanced fitness evaluation with multiple complexity metrics
            """
            # Extract performance metrics
            performance_metrics = self._extract_performance_metrics(attack_results)
            
            # Compute multi-dimensional fitness score
            fitness_components = {
                'dimensional_diversity': len(strategy['dimensions']) / 5.0,
                'complexity_utilization': strategy['complexity_score'],
                'mutation_adaptability': strategy['mutation_potential'],
                'attack_success_rate': performance_metrics.get('success_rate', 0),
                'information_gain': self._compute_information_gain(attack_results),
                'strategy_entropy': self._compute_strategy_entropy(strategy)
            }
            
            # Normalize and weight fitness components
            normalized_components = self.scaler.fit_transform(
                np.array(list(fitness_components.values())).reshape(1, -1)
            )[0]
            
            # Weighted fitness computation
            weights = [0.2, 0.15, 0.15, 0.2, 0.15, 0.15]
            comprehensive_fitness = np.dot(normalized_components, weights)
            
            return {
                'comprehensive_fitness': comprehensive_fitness,
                'fitness_components': dict(zip(fitness_components.keys(), normalized_components))
            }
        
        def _extract_performance_metrics(
            self, 
            attack_results: List[Dict[str, Any]]
        ) -> Dict[str, float]:
            """
            Extract nuanced performance metrics from attack results
            """
            if not attack_results:
                return {}
            
            success_vulnerabilities = [
                result['vulnerability_score'] 
                for result in attack_results 
                if result['vulnerability_score'] > 0.5
            ]
            
            return {
                'success_rate': len(success_vulnerabilities) / len(attack_results),
                'average_vulnerability': np.mean(
                    [result['vulnerability_score'] for result in attack_results]
                ),
                'max_vulnerability': np.max(
                    [result['vulnerability_score'] for result in attack_results]
                )
            }
        
        def _compute_information_gain(
            self, 
            attack_results: List[Dict[str, Any]]
        ) -> float:
            """
            Compute information theoretic metrics of attack results
            """
            vulnerability_scores = [
                result['vulnerability_score'] 
                for result in attack_results
            ]
            
            # Compute mutual information
            return mutual_info_score(
                vulnerability_scores, 
                np.random.permutation(vulnerability_scores)
            )
        
        def _compute_strategy_entropy(
            self, 
            strategy: Dict[str, Any]
        ) -> float:
            """
            Compute entropy of attack strategy configuration
            """
            strategy_vector = [
                len(strategy['dimensions']),
                strategy['complexity_score'],
                strategy['mutation_potential']
            ]
            
            return entropy(strategy_vector)

    class InteractiveVisualizationEngine:
        """
        Advanced Interactive Visualization Tools
        """
        @staticmethod
        def create_interactive_strategy_explorer(
            attack_strategies: List[Dict[str, Any]]
        ):
            """
            Create interactive Plotly-based strategy exploration dashboard
            """
            # Extract strategy features
            strategy_df = pd.DataFrame([
                {
                    'Dimensions': len(strategy['dimensions']),
                    'Complexity': strategy['complexity_score'],
                    'Mutation Potential': strategy['mutation_potential'],
                    'Entropy Coefficient': strategy['entropy_coefficient']
                } for strategy in attack_strategies
            ])
            
            # Create interactive scatter plot
            fig = px.scatter_matrix(
                strategy_df, 
                dimensions=strategy_df.columns,
                color_continuous_scale=px.colors.sequential.Viridis
            )
            
            fig.update_layout(
                title='Interactive Attack Strategy Explorer',
                height=1000,
                width=1000
            )
            
            fig.show()
        
        @staticmethod
        def generate_3d_strategy_visualization(
            attack_strategies: List[Dict[str, Any]]
        ):
            """
            Create 3D visualization of attack strategy space
            """
            # Extract features
            dimensions = [len(strategy['dimensions']) for strategy in attack_strategies]
            complexity = [strategy['complexity_score'] for strategy in attack_strategies]
            mutation_potential = [strategy['mutation_potential'] for strategy in attack_strategies]
            
            # Create 3D scatter plot
            fig = go.Figure(data=[go.Scatter3d(
                x=dimensions,
                y=complexity,
                z=mutation_potential,
                mode='markers',
                marker=dict(
                    size=5,
                    color=complexity,
                    colorscale='Viridis',
                    opacity=0.8
                )
            )])
            
            fig.update_layout(
                title='3D Attack Strategy Space Visualization',
                scene=dict(
                    xaxis_title='Dimensional Diversity',
                    yaxis_title='Complexity Score',
                    zaxis_title='Mutation Potential'
                )
            )
            
            fig.show()

def main():
    # Create framework components
    knowledge_graph = AdvancedAttackStrategyIntegrationFramework.AttackStrategyKnowledgeGraph()
    fitness_evaluator = AdvancedAttackStrategyIntegrationFramework.AdvancedFitnessEvaluator()
    visualization_engine = AdvancedAttackStrategyIntegrationFramework.InteractiveVisualizationEngine()
    
    # Sample attack strategies
    attack_strategies = [
        {
            'dimensions': ['semantic_manipulation', 'cognitive_exploitation'],
            'complexity_score': 0.7,
            'mutation_potential': 0.5,
            'entropy_coefficient': 0.6
        },
        # Add more strategies...
    ]
    
    # Simulate attack results
    simulated_attack_results = [
        {'vulnerability_score': np.random.random()} for _ in range(10)
    ]
    
    # Evaluate fitness of strategies
    fitness_results = {}
    for strategy in attack_strategies:
        fitness_results[str(strategy)] = fitness_evaluator.compute_comprehensive_fitness(
            strategy, 
            simulated_attack_results
        )
        
        # Add to knowledge graph
        knowledge_graph.add_attack_strategy_knowledge(
            strategy, 
            fitness_results[str(strategy)]
        )
    
    # Analyze strategy relationships
    strategy_analysis = knowledge_graph.analyze_strategy_relationships()
    
    # Interactive visualizations
    visualization_engine.create_interactive_strategy_explorer(attack_strategies)
    visualization_engine.generate_3d_strategy_visualization(attack_strategies)

if __name__ == "__main__":
    main()