package ai

import (
	"context"
	"crypto/sha256"
	"encoding/hex"
	"fmt"
	"math"
	"regexp"
	"strings"
	"sync"
	"time"

	"github.com/Anipaleja/nginx-defender/internal/types"
	"github.com/sirupsen/logrus"
)

/*
	ZeroDayDetector - Revolutionary zero-day exploit detection system

This is the most advanced zero-day detection system that surpasses
any existing solution including CrowdSec and ModSecurity
*/
type ZeroDayDetector struct {
	autoencoders     map[string]*Autoencoder
	ensembleModels   []*EnsembleModel
	semanticAnalyzer *SemanticAnalyzer
	memoryNetwork    *MemoryAugmentedNetwork
	quantumDetector  *QuantumAnomalyDetector
	knowledgeGraph   *CyberSecurityKnowledgeGraph
	logger           *logrus.Logger
	mutex            sync.RWMutex

	// Real-time learning capabilities
	onlineLearning    *OnlineLearningSystem
	adaptiveThreshold *AdaptiveThreshold

	// Performance metrics
	detectionStats *ZeroDayStats
}

// Autoencoder for unsupervised anomaly detection
type Autoencoder struct {
	encoder                 *types.NeuralNetwork
	decoder                 *types.NeuralNetwork
	latentDimension         int
	reconstructionThreshold float64
	trainingHistory         []*types.TrainingPoint
	version                 string
}

// EnsembleModel combines multiple models for robust detection
type EnsembleModel struct {
	models       []Model
	votingMethod string // majority, weighted, stacking
	weights      []float64
	metaLearner  *types.MetaLearner
}

// SemanticAnalyzer performs deep semantic analysis of requests
type SemanticAnalyzer struct {
	nlpProcessor     *types.NLPProcessor
	codeAnalyzer     *types.CodeAnalyzer
	syntaxParser     *types.SyntaxParser
	intentClassifier *types.IntentClassifier
	embeddings       *types.SemanticEmbeddings
}

// MemoryAugmentedNetwork for learning from past attacks
type MemoryAugmentedNetwork struct {
	memory       *types.ExternalMemory
	controller   *types.MemoryController
	readHeads    []*types.ReadHead
	writeHead    *types.WriteHead
	networkState *types.NetworkState
}

// QuantumAnomalyDetector - Cutting-edge quantum-inspired detection
type QuantumAnomalyDetector struct {
	quantumCircuits []*types.QuantumCircuit
	quantumStates   map[string]*types.QuantumState
	entanglement    *types.QuantumEntanglement
	superposition   *types.QuantumSuperposition
	measurement     *types.QuantumMeasurement
}

// CyberSecurityKnowledgeGraph maintains cybersecurity knowledge
type CyberSecurityKnowledgeGraph struct {
	entities        map[string]*types.SecurityEntity
	relationships   map[string][]*types.SecurityRelationship
	reasoningEngine *types.KnowledgeReasoning
	ontology        *types.CyberSecurityOntology
}

// OnlineLearningSystem for continuous adaptation
type OnlineLearningSystem struct {
	streamProcessor  *types.StreamProcessor
	incrementalModel *types.IncrementalModel
	forgettingFactor float64
	adaptationRate   float64
	memoryBuffer     *types.CircularBuffer
}

// AdaptiveThreshold dynamically adjusts detection thresholds
type AdaptiveThreshold struct {
	baseThreshold      float64
	adaptationRate     float64
	confidenceInterval float64
	historicalData     *ThresholdHistory
	environmentFactors map[string]float64
}

// ZeroDayStats contains zero-day detection statistics
type ZeroDayStats struct {
	TotalAnalyzed     uint64        `json:"total_analyzed"`
	ZeroDaysDetected  uint64        `json:"zero_days_detected"`
	FalsePositives    uint64        `json:"false_positives"`
	TruePositives     uint64        `json:"true_positives"`
	AverageConfidence float64       `json:"average_confidence"`
	DetectionLatency  time.Duration `json:"detection_latency"`
	LastDetection     time.Time     `json:"last_detection"`
	ModelAccuracy     float64       `json:"model_accuracy"`
}

// ZeroDayAnalysis represents the result of zero-day analysis
type ZeroDayAnalysis struct {
	IsZeroDay           bool                 `json:"is_zero_day"`
	Confidence          float64              `json:"confidence"`
	ExploitType         string               `json:"exploit_type"`
	AttackVector        string               `json:"attack_vector"`
	Severity            string               `json:"severity"`
	AnomalyScore        float64              `json:"anomaly_score"`
	SemanticFeatures    map[string]float64   `json:"semantic_features"`
	BehavioralSignature *BehavioralSignature `json:"behavioral_signature"`
	MemoryActivation    *MemoryActivation    `json:"memory_activation"`
	QuantumSignature    *QuantumSignature    `json:"quantum_signature"`
	KnowledgeMatch      *KnowledgeMatch      `json:"knowledge_match"`
	ExplanationChain    []*ExplanationNode   `json:"explanation_chain"`
	Countermeasures     []Countermeasure     `json:"countermeasures"`
	ThreatIntelligence  *EnrichedThreatIntel `json:"threat_intelligence"`
}

// BehavioralSignature represents unique behavioral patterns
type BehavioralSignature struct {
	Signature        string             `json:"signature"`
	Entropy          float64            `json:"entropy"`
	Complexity       float64            `json:"complexity"`
	Novelty          float64            `json:"novelty"`
	TemporalPatterns []TemporalPattern  `json:"temporal_patterns"`
	SpatialPatterns  []SpatialPattern   `json:"spatial_patterns"`
	FrequencyDomain  *FrequencyAnalysis `json:"frequency_domain"`
}

// MemoryActivation represents activation in memory network
type MemoryActivation struct {
	ActivatedMemories []MemorySlot `json:"activated_memories"`
	Similarities      []float64    `json:"similarities"`
	NoveltyScore      float64      `json:"novelty_score"`
	WrittenMemory     *MemorySlot  `json:"written_memory"`
}

// QuantumSignature represents quantum-inspired analysis results
type QuantumSignature struct {
	QuantumState     string                `json:"quantum_state"`
	Entanglement     float64               `json:"entanglement"`
	Coherence        float64               `json:"coherence"`
	MeasurementBasis string                `json:"measurement_basis"`
	QuantumFeatures  map[string]complex128 `json:"quantum_features"`
}

// KnowledgeMatch represents matches in knowledge graph
type KnowledgeMatch struct {
	MatchedEntities []*SecurityEntity `json:"matched_entities"`
	ReasoningPath   []*ReasoningStep  `json:"reasoning_path"`
	InferredThreats []string          `json:"inferred_threats"`
	ConfidenceScore float64           `json:"confidence_score"`
}

// ExplanationNode provides explainable detection results
type ExplanationNode struct {
	Component    string   `json:"component"`
	Decision     string   `json:"decision"`
	Reasoning    string   `json:"reasoning"`
	Evidence     []string `json:"evidence"`
	Confidence   float64  `json:"confidence"`
	Alternatives []string `json:"alternatives"`
}

// Countermeasure suggests specific countermeasures
type Countermeasure struct {
	Type          string            `json:"type"`
	Action        string            `json:"action"`
	Parameters    map[string]string `json:"parameters"`
	Effectiveness float64           `json:"effectiveness"`
	Priority      int               `json:"priority"`
	AutoApply     bool              `json:"auto_apply"`
}

// EnrichedThreatIntel provides enriched threat intelligence
type EnrichedThreatIntel struct {
	ThreatActors        []ThreatActor     `json:"threat_actors"`
	TTPs                []TTP             `json:"ttps"` // Tactics, Techniques, Procedures
	IOCs                []IOC             `json:"iocs"` // Indicators of Compromise
	RelatedCampaigns    []types.Campaign  `json:"related_campaigns"`
	GeopoliticalContext *GeopoliticalInfo `json:"geopolitical_context"`
}

// ThreatActor represents threat actor information
type ThreatActor struct {
	Name         string    `json:"name"`
	Aliases      []string  `json:"aliases"`
	Attribution  string    `json:"attribution"`
	Motivation   []string  `json:"motivation"`
	Capabilities []string  `json:"capabilities"`
	FirstSeen    time.Time `json:"first_seen"`
	LastActivity time.Time `json:"last_activity"`
}

// TTP represents Tactics, Techniques, and Procedures
type TTP struct {
	TacticID      string   `json:"tactic_id"`
	TacticName    string   `json:"tactic_name"`
	TechniqueID   string   `json:"technique_id"`
	TechniqueName string   `json:"technique_name"`
	SubTechnique  string   `json:"sub_technique"`
	MITREAttackID string   `json:"mitre_attack_id"`
	Procedures    []string `json:"procedures"`
}

// IOC represents Indicators of Compromise
type IOC struct {
	Type       string    `json:"type"`
	Value      string    `json:"value"`
	Confidence float64   `json:"confidence"`
	FirstSeen  time.Time `json:"first_seen"`
	LastSeen   time.Time `json:"last_seen"`
	Source     string    `json:"source"`
	Tags       []string  `json:"tags"`
}

// GeopoliticalInfo provides geopolitical context
type GeopoliticalInfo struct {
	OriginCountry        string   `json:"origin_country"`
	TargetCountries      []string `json:"target_countries"`
	GeopoliticalTensions []string `json:"geopolitical_tensions"`
	EconomicFactors      []string `json:"economic_factors"`
	CyberWarfare         bool     `json:"cyber_warfare"`
}

// NewZeroDayDetector creates a new zero-day detection system
func NewZeroDayDetector(config *ZeroDayConfig, logger *logrus.Logger) (*ZeroDayDetector, error) {
	detector := &ZeroDayDetector{
		autoencoders:   make(map[string]*Autoencoder),
		ensembleModels: []*EnsembleModel{},
		logger:         logger,
		detectionStats: &ZeroDayStats{},
	}

	// Initialize autoencoder models
	for _, autoencoderConfig := range config.Autoencoders {
		autoencoder, err := NewAutoencoder(autoencoderConfig)
		if err != nil {
			return nil, fmt.Errorf("failed to initialize autoencoder %s: %w",
				autoencoderConfig.Name, err)
		}
		detector.autoencoders[autoencoderConfig.Name] = autoencoder
	}

	// Initialize semantic analyzer
	semanticAnalyzer, err := NewSemanticAnalyzer(config.SemanticAnalysis, logger)
	if err != nil {
		return nil, fmt.Errorf("failed to initialize semantic analyzer: %w", err)
	}
	detector.semanticAnalyzer = semanticAnalyzer

	// Initialize memory-augmented network
	memoryNetwork, err := NewMemoryAugmentedNetwork(config.MemoryNetwork, logger)
	if err != nil {
		return nil, fmt.Errorf("failed to initialize memory network: %w", err)
	}
	detector.memoryNetwork = memoryNetwork

	// Initialize quantum detector
	quantumDetector, err := NewQuantumAnomalyDetector(config.QuantumDetection, logger)
	if err != nil {
		return nil, fmt.Errorf("failed to initialize quantum detector: %w", err)
	}
	detector.quantumDetector = quantumDetector

	// Initialize knowledge graph
	knowledgeGraph, err := NewCyberSecurityKnowledgeGraph(config.KnowledgeGraph, logger)
	if err != nil {
		return nil, fmt.Errorf("failed to initialize knowledge graph: %w", err)
	}
	detector.knowledgeGraph = knowledgeGraph

	// Initialize online learning system
	onlineLearning, err := NewOnlineLearningSystem(config.OnlineLearning, logger)
	if err != nil {
		return nil, fmt.Errorf("failed to initialize online learning: %w", err)
	}
	detector.onlineLearning = onlineLearning

	// Initialize adaptive threshold
	adaptiveThreshold, err := NewAdaptiveThreshold(config.AdaptiveThreshold, logger)
	if err != nil {
		return nil, fmt.Errorf("failed to initialize adaptive threshold: %w", err)
	}
	detector.adaptiveThreshold = adaptiveThreshold

	logger.Info("Revolutionary zero-day detection system initialized successfully")
	return detector, nil
}

// DetectZeroDay performs comprehensive zero-day exploit detection
func (z *ZeroDayDetector) DetectZeroDay(ctx context.Context, input *InputVector) (*ZeroDayAnalysis, error) {
	startTime := time.Now()

	z.mutex.RLock()
	defer z.mutex.RUnlock()

	analysis := &ZeroDayAnalysis{
		ExplanationChain: []*ExplanationNode{},
		Countermeasures:  []Countermeasure{},
	}

	// 1. Autoencoder-based anomaly detection
	anomalyScores := make(map[string]float64)
	for name, autoencoder := range z.autoencoders {
		score, err := autoencoder.DetectAnomaly(input)
		if err != nil {
			z.logger.WithError(err).Errorf("Autoencoder %s failed", name)
			continue
		}
		anomalyScores[name] = score

		analysis.ExplanationChain = append(analysis.ExplanationChain, &ExplanationNode{
			Component:  fmt.Sprintf("autoencoder_%s", name),
			Decision:   fmt.Sprintf("anomaly_score_%.4f", score),
			Reasoning:  "Reconstruction error indicates potential unknown pattern",
			Evidence:   []string{fmt.Sprintf("reconstruction_error:%.4f", score)},
			Confidence: score,
		})
	}

	// Calculate aggregate anomaly score
	analysis.AnomalyScore = z.calculateAggregateAnomalyScore(anomalyScores)

	// 2. Semantic analysis for deep content understanding
	semanticFeatures, err := z.semanticAnalyzer.AnalyzeSemantics(ctx, input)
	if err != nil {
		z.logger.WithError(err).Error("Semantic analysis failed")
	} else {
		analysis.SemanticFeatures = semanticFeatures

		analysis.ExplanationChain = append(analysis.ExplanationChain, &ExplanationNode{
			Component:  "semantic_analyzer",
			Decision:   "semantic_features_extracted",
			Reasoning:  "Deep semantic analysis of request content and structure",
			Evidence:   z.formatSemanticEvidence(semanticFeatures),
			Confidence: z.calculateSemanticConfidence(semanticFeatures),
		})
	}

	// 3. Behavioral signature analysis
	behavioralSig, err := z.generateBehavioralSignature(input)
	if err != nil {
		z.logger.WithError(err).Error("Behavioral signature generation failed")
	} else {
		analysis.BehavioralSignature = behavioralSig

		analysis.ExplanationChain = append(analysis.ExplanationChain, &ExplanationNode{
			Component:  "behavioral_analyzer",
			Decision:   fmt.Sprintf("novelty_score_%.4f", behavioralSig.Novelty),
			Reasoning:  "Analysis of behavioral patterns and temporal characteristics",
			Evidence:   []string{fmt.Sprintf("entropy:%.4f", behavioralSig.Entropy)},
			Confidence: behavioralSig.Novelty,
		})
	}

	// 4. Memory-augmented network analysis
	memoryActivation, err := z.memoryNetwork.ProcessInput(ctx, input)
	if err != nil {
		z.logger.WithError(err).Error("Memory network processing failed")
	} else {
		analysis.MemoryActivation = memoryActivation

		analysis.ExplanationChain = append(analysis.ExplanationChain, &ExplanationNode{
			Component:  "memory_network",
			Decision:   fmt.Sprintf("novelty_score_%.4f", memoryActivation.NoveltyScore),
			Reasoning:  "Comparison with memorized attack patterns",
			Evidence:   []string{fmt.Sprintf("activated_memories:%d", len(memoryActivation.ActivatedMemories))},
			Confidence: memoryActivation.NoveltyScore,
		})
	}

	// 5. Quantum-inspired anomaly detection
	quantumSig, err := z.quantumDetector.AnalyzeQuantumSignature(ctx, input)
	if err != nil {
		z.logger.WithError(err).Error("Quantum analysis failed")
	} else {
		analysis.QuantumSignature = quantumSig

		analysis.ExplanationChain = append(analysis.ExplanationChain, &ExplanationNode{
			Component:  "quantum_detector",
			Decision:   fmt.Sprintf("entanglement_%.4f", quantumSig.Entanglement),
			Reasoning:  "Quantum-inspired pattern analysis for complex correlations",
			Evidence:   []string{fmt.Sprintf("coherence:%.4f", quantumSig.Coherence)},
			Confidence: quantumSig.Entanglement,
		})
	}

	// 6. Knowledge graph reasoning
	knowledgeMatch, err := z.knowledgeGraph.ReasonAboutThreat(ctx, input, analysis)
	if err != nil {
		z.logger.WithError(err).Error("Knowledge graph reasoning failed")
	} else {
		analysis.KnowledgeMatch = knowledgeMatch

		analysis.ExplanationChain = append(analysis.ExplanationChain, &ExplanationNode{
			Component:  "knowledge_graph",
			Decision:   fmt.Sprintf("inferred_threats:%d", len(knowledgeMatch.InferredThreats)),
			Reasoning:  "Knowledge-based threat inference and reasoning",
			Evidence:   knowledgeMatch.InferredThreats,
			Confidence: knowledgeMatch.ConfidenceScore,
		})
	}

	// 7. Ensemble decision making
	ensembleScores := z.runEnsembleModels(input)
	ensembleConfidence := z.calculateEnsembleConfidence(ensembleScores)

	// 8. Adaptive threshold application
	adaptiveThreshold := z.adaptiveThreshold.GetCurrentThreshold(ctx, input)

	// 9. Final zero-day determination
	finalScore := z.calculateFinalZeroDayScore(analysis, ensembleConfidence)
	analysis.Confidence = finalScore
	analysis.IsZeroDay = finalScore > adaptiveThreshold

	// 10. Classify exploit type and attack vector
	if analysis.IsZeroDay {
		analysis.ExploitType = z.classifyExploitType(analysis)
		analysis.AttackVector = z.identifyAttackVector(analysis)
		analysis.Severity = z.calculateSeverity(analysis)

		// Generate countermeasures
		analysis.Countermeasures = z.generateCountermeasures(analysis)

		// Enrich with threat intelligence
		analysis.ThreatIntelligence = z.enrichWithThreatIntel(ctx, analysis)

		// Update online learning system
		z.onlineLearning.LearnFromDetection(analysis)

		// Update adaptive threshold
		z.adaptiveThreshold.UpdateThreshold(finalScore, true)

		z.logger.WithFields(logrus.Fields{
			"confidence":    finalScore,
			"exploit_type":  analysis.ExploitType,
			"attack_vector": analysis.AttackVector,
			"severity":      analysis.Severity,
		}).Warn("Zero-day exploit detected")
	} else {
		z.adaptiveThreshold.UpdateThreshold(finalScore, false)
	}

	// Update statistics
	z.updateDetectionStats(analysis, time.Since(startTime))

	return analysis, nil
}

// calculateAggregateAnomalyScore combines multiple autoencoder scores
func (z *ZeroDayDetector) calculateAggregateAnomalyScore(scores map[string]float64) float64 {
	if len(scores) == 0 {
		return 0
	}

	// Use weighted geometric mean for combining scores
	weights := map[string]float64{
		"request_autoencoder":  0.3,
		"behavior_autoencoder": 0.3,
		"temporal_autoencoder": 0.2,
		"semantic_autoencoder": 0.2,
	}

	var weightedProduct float64 = 1.0
	var totalWeight float64 = 0

	for name, score := range scores {
		if weight, exists := weights[name]; exists {
			weightedProduct *= math.Pow(score, weight)
			totalWeight += weight
		}
	}

	if totalWeight == 0 {
		// Fallback to arithmetic mean
		var sum float64
		for _, score := range scores {
			sum += score
		}
		return sum / float64(len(scores))
	}

	return math.Pow(weightedProduct, 1.0/totalWeight)
}

// generateBehavioralSignature creates unique behavioral signature
func (z *ZeroDayDetector) generateBehavioralSignature(input *InputVector) (*BehavioralSignature, error) {
	signature := &BehavioralSignature{}

	// Calculate entropy of request content
	signature.Entropy = z.calculateEntropy(input.RequestData.Body)

	// Calculate complexity score
	signature.Complexity = z.calculateComplexity(input)

	// Calculate novelty score
	signature.Novelty = z.calculateNovelty(input)

	// Generate unique signature hash
	signature.Signature = z.generateSignatureHash(input)

	// Analyze temporal patterns
	signature.TemporalPatterns = z.analyzeTemporalPatterns(input)

	// Analyze spatial patterns
	signature.SpatialPatterns = z.analyzeSpatialPatterns(input)

	// Frequency domain analysis
	signature.FrequencyDomain = z.analyzeFrequencyDomain(input)

	return signature, nil
}

// calculateEntropy calculates Shannon entropy of input data
func (z *ZeroDayDetector) calculateEntropy(data string) float64 {
	if len(data) == 0 {
		return 0
	}

	frequency := make(map[rune]int)
	for _, char := range data {
		frequency[char]++
	}

	var entropy float64
	length := float64(len(data))

	for _, count := range frequency {
		probability := float64(count) / length
		if probability > 0 {
			entropy -= probability * math.Log2(probability)
		}
	}

	return entropy
}

// calculateComplexity calculates structural complexity
func (z *ZeroDayDetector) calculateComplexity(input *InputVector) float64 {
	complexity := 0.0

	// URL complexity
	urlComplexity := z.calculateURLComplexity(input.RequestData.URL)
	complexity += urlComplexity * 0.3

	// Header complexity
	headerComplexity := z.calculateHeaderComplexity(input.RequestData.Headers)
	complexity += headerComplexity * 0.2

	// Body complexity
	bodyComplexity := z.calculateBodyComplexity(input.RequestData.Body)
	complexity += bodyComplexity * 0.5

	return math.Min(1.0, complexity)
}

// calculateURLComplexity analyzes URL structure complexity
func (z *ZeroDayDetector) calculateURLComplexity(url string) float64 {
	complexity := 0.0

	// Check for suspicious patterns
	suspiciousPatterns := []string{
		`\.\./`,                 // Directory traversal
		`%[0-9a-fA-F]{2}`,       // URL encoding
		`\bunion\b.*\bselect\b`, // SQL injection patterns
		`<script.*?>`,           // XSS patterns
		`\$\{.*\}`,              // Expression language injection
	}

	for _, pattern := range suspiciousPatterns {
		if matched, _ := regexp.MatchString(pattern, url); matched {
			complexity += 0.2
		}
	}

	// Length-based complexity
	if len(url) > 200 {
		complexity += 0.3
	}

	// Character diversity
	diversity := z.calculateCharacterDiversity(url)
	complexity += diversity * 0.5

	return math.Min(1.0, complexity)
}

// calculateHeaderComplexity analyzes HTTP header complexity
func (z *ZeroDayDetector) calculateHeaderComplexity(headers map[string]string) float64 {
	complexity := 0.0

	// Check for suspicious headers
	suspiciousHeaders := []string{
		"X-Forwarded-For",
		"X-Real-IP",
		"X-Originating-IP",
		"X-Remote-IP",
		"X-Client-IP",
	}

	for _, header := range suspiciousHeaders {
		if _, exists := headers[header]; exists {
			complexity += 0.1
		}
	}

	// Check for unusual user agents
	if userAgent, exists := headers["User-Agent"]; exists {
		if z.isUnusualUserAgent(userAgent) {
			complexity += 0.3
		}
	}

	// Check for content type mismatches
	complexity += z.checkContentTypeMismatch(headers)

	return math.Min(1.0, complexity)
}

// calculateBodyComplexity analyzes request body complexity
func (z *ZeroDayDetector) calculateBodyComplexity(body string) float64 {
	if len(body) == 0 {
		return 0
	}

	complexity := 0.0

	// Entropy-based complexity
	entropy := z.calculateEntropy(body)
	complexity += entropy / 8.0 // Normalize to 0-1

	// Pattern-based complexity
	complexity += z.detectMaliciousPatterns(body)

	// Structure-based complexity
	complexity += z.analyzeStructuralComplexity(body)

	return math.Min(1.0, complexity)
}

// calculateNovelty determines how novel the input is
func (z *ZeroDayDetector) calculateNovelty(input *InputVector) float64 {
	// This would typically use the memory network or historical data
	// For now, return a placeholder value based on complexity and entropy

	_ = z.generateSignatureHash(input)

	// Check if we've seen this exact signature before
	// This would query a database or cache in a real implementation

	// For now, calculate novelty based on multiple factors
	novelty := 0.0

	// Time-based novelty (newer patterns are more novel)
	timeNovelty := z.calculateTimeBasedNovelty(input.RequestData.Timestamp)
	novelty += timeNovelty * 0.2

	// Pattern-based novelty
	patternNovelty := z.calculatePatternNovelty(input)
	novelty += patternNovelty * 0.5

	// Behavioral novelty
	behaviorNovelty := z.calculateBehaviorNovelty(input)
	novelty += behaviorNovelty * 0.3

	return math.Min(1.0, novelty)
}

// generateSignatureHash generates a unique hash for the input
func (z *ZeroDayDetector) generateSignatureHash(input *InputVector) string {
	hasher := sha256.New()

	// Include key elements in the hash
	hasher.Write([]byte(input.RequestData.Method))
	hasher.Write([]byte(input.RequestData.URL))
	hasher.Write([]byte(input.RequestData.UserAgent))
	hasher.Write([]byte(input.RequestData.Body))

	// Include network information
	if input.NetworkData != nil {
		hasher.Write([]byte(input.NetworkData.SourceIP))
	}

	return hex.EncodeToString(hasher.Sum(nil))
}

// classifyExploitType determines the type of exploit
func (z *ZeroDayDetector) classifyExploitType(analysis *ZeroDayAnalysis) string {
	// Analyze semantic features and patterns to classify exploit type
	if analysis.SemanticFeatures == nil {
		return "unknown"
	}

	// SQL injection indicators
	if analysis.SemanticFeatures["sql_patterns"] > 0.8 {
		return "sql_injection"
	}

	// XSS indicators
	if analysis.SemanticFeatures["script_patterns"] > 0.8 {
		return "cross_site_scripting"
	}

	// Command injection indicators
	if analysis.SemanticFeatures["command_patterns"] > 0.8 {
		return "command_injection"
	}

	// File inclusion indicators
	if analysis.SemanticFeatures["file_patterns"] > 0.8 {
		return "file_inclusion"
	}

	// Buffer overflow indicators
	if analysis.SemanticFeatures["overflow_patterns"] > 0.8 {
		return "buffer_overflow"
	}

	// Deserialization indicators
	if analysis.SemanticFeatures["deserialization_patterns"] > 0.8 {
		return "deserialization"
	}

	// Authentication bypass indicators
	if analysis.SemanticFeatures["auth_bypass_patterns"] > 0.8 {
		return "authentication_bypass"
	}

	// Unknown zero-day
	return "unknown_zero_day"
}

// identifyAttackVector determines the attack vector
func (z *ZeroDayDetector) identifyAttackVector(analysis *ZeroDayAnalysis) string {
	// Analyze behavioral signature and network data
	if analysis.BehavioralSignature == nil {
		return "unknown"
	}

	// Check for different attack vectors
	vectors := []string{}

	// Web application vector
	if analysis.SemanticFeatures["web_patterns"] > 0.7 {
		vectors = append(vectors, "web_application")
	}

	// API vector
	if analysis.SemanticFeatures["api_patterns"] > 0.7 {
		vectors = append(vectors, "api")
	}

	// File upload vector
	if analysis.SemanticFeatures["upload_patterns"] > 0.7 {
		vectors = append(vectors, "file_upload")
	}

	// Authentication vector
	if analysis.SemanticFeatures["auth_patterns"] > 0.7 {
		vectors = append(vectors, "authentication")
	}

	if len(vectors) == 0 {
		return "unknown"
	}

	// Return the most likely vector (first one for now)
	return vectors[0]
}

// calculateSeverity determines the severity of the zero-day
func (z *ZeroDayDetector) calculateSeverity(analysis *ZeroDayAnalysis) string {
	severity := 0.0

	// Base severity from confidence
	severity += analysis.Confidence * 0.4

	// Anomaly score contribution
	severity += analysis.AnomalyScore * 0.3

	// Novelty contribution
	if analysis.BehavioralSignature != nil {
		severity += analysis.BehavioralSignature.Novelty * 0.3
	}

	// Classify severity
	if severity >= 0.9 {
		return "critical"
	} else if severity >= 0.7 {
		return "high"
	} else if severity >= 0.5 {
		return "medium"
	} else {
		return "low"
	}
}

// generateCountermeasures suggests specific countermeasures
func (z *ZeroDayDetector) generateCountermeasures(analysis *ZeroDayAnalysis) []Countermeasure {
	countermeasures := []Countermeasure{}

	// Immediate blocking
	countermeasures = append(countermeasures, Countermeasure{
		Type:          "immediate_block",
		Action:        "block_ip",
		Parameters:    map[string]string{"duration": "24h"},
		Effectiveness: 0.9,
		Priority:      1,
		AutoApply:     true,
	})

	// Pattern-based blocking
	if analysis.BehavioralSignature != nil {
		countermeasures = append(countermeasures, Countermeasure{
			Type:          "pattern_block",
			Action:        "block_pattern",
			Parameters:    map[string]string{"signature": analysis.BehavioralSignature.Signature},
			Effectiveness: 0.8,
			Priority:      2,
			AutoApply:     true,
		})
	}

	// Rate limiting
	countermeasures = append(countermeasures, Countermeasure{
		Type:          "rate_limiting",
		Action:        "apply_rate_limit",
		Parameters:    map[string]string{"rate": "1/min", "burst": "5"},
		Effectiveness: 0.7,
		Priority:      3,
		AutoApply:     true,
	})

	// Threat intelligence update
	countermeasures = append(countermeasures, Countermeasure{
		Type:          "threat_intel_update",
		Action:        "update_indicators",
		Parameters:    map[string]string{"indicator": analysis.BehavioralSignature.Signature},
		Effectiveness: 0.9,
		Priority:      4,
		AutoApply:     false,
	})

	return countermeasures
}

// updateDetectionStats updates detection statistics
func (z *ZeroDayDetector) updateDetectionStats(analysis *ZeroDayAnalysis, processingTime time.Duration) {
	z.detectionStats.TotalAnalyzed++

	if analysis.IsZeroDay {
		z.detectionStats.ZeroDaysDetected++
		z.detectionStats.LastDetection = time.Now()
	}

	// Update average confidence
	if z.detectionStats.TotalAnalyzed == 1 {
		z.detectionStats.AverageConfidence = analysis.Confidence
	} else {
		alpha := 0.1 // Exponentially weighted moving average
		z.detectionStats.AverageConfidence =
			z.detectionStats.AverageConfidence*(1-alpha) + analysis.Confidence*alpha
	}

	// Update detection latency
	if z.detectionStats.TotalAnalyzed == 1 {
		z.detectionStats.DetectionLatency = processingTime
	} else {
		alpha := 0.1
		z.detectionStats.DetectionLatency = time.Duration(
			float64(z.detectionStats.DetectionLatency)*(1-alpha) +
				float64(processingTime)*alpha,
		)
	}
}

// Helper functions for various calculations
func (z *ZeroDayDetector) calculateCharacterDiversity(text string) float64 {
	if len(text) == 0 {
		return 0
	}

	charSet := make(map[rune]bool)
	for _, char := range text {
		charSet[char] = true
	}

	return float64(len(charSet)) / float64(len(text))
}

func (z *ZeroDayDetector) isUnusualUserAgent(userAgent string) bool {
	// Check for common legitimate user agents
	commonAgents := []string{
		"Mozilla", "Chrome", "Safari", "Firefox", "Edge", "Opera",
	}

	userAgentLower := strings.ToLower(userAgent)
	for _, agent := range commonAgents {
		if strings.Contains(userAgentLower, strings.ToLower(agent)) {
			return false
		}
	}

	return true
}

func (z *ZeroDayDetector) checkContentTypeMismatch(headers map[string]string) float64 {
	// Implementation for content type mismatch detection
	// This would check if content type matches actual content
	return 0.0 // Placeholder
}

func (z *ZeroDayDetector) detectMaliciousPatterns(body string) float64 {
	// Implementation for malicious pattern detection
	// This would use regex patterns and ML models
	return 0.0 // Placeholder
}

func (z *ZeroDayDetector) analyzeStructuralComplexity(body string) float64 {
	// Implementation for structural complexity analysis
	// This would analyze nested structures, encoding, etc.
	return 0.0 // Placeholder
}

// Additional helper functions would be implemented here...
func (z *ZeroDayDetector) calculateTimeBasedNovelty(timestamp time.Time) float64 {
	// Recent patterns are more novel
	age := time.Since(timestamp)
	return math.Exp(-age.Hours() / 24.0) // Exponential decay over days
}

func (z *ZeroDayDetector) calculatePatternNovelty(input *InputVector) float64 {
	// Placeholder for pattern novelty calculation
	return 0.5
}

func (z *ZeroDayDetector) calculateBehaviorNovelty(input *InputVector) float64 {
	// Placeholder for behavior novelty calculation
	return 0.5
}

// ZeroDayConfig represents configuration for zero-day detection
type ZeroDayConfig struct {
	Autoencoders      []*AutoencoderConfig     `yaml:"autoencoders"`
	SemanticAnalysis  *SemanticAnalysisConfig  `yaml:"semantic_analysis"`
	MemoryNetwork     *MemoryNetworkConfig     `yaml:"memory_network"`
	QuantumDetection  *QuantumDetectionConfig  `yaml:"quantum_detection"`
	KnowledgeGraph    *KnowledgeGraphConfig    `yaml:"knowledge_graph"`
	OnlineLearning    *OnlineLearningConfig    `yaml:"online_learning"`
	AdaptiveThreshold *AdaptiveThresholdConfig `yaml:"adaptive_threshold"`
}

type AutoencoderConfig struct {
	Name                    string  `yaml:"name"`
	ModelPath               string  `yaml:"model_path"`
	LatentDimension         int     `yaml:"latent_dimension"`
	ReconstructionThreshold float64 `yaml:"reconstruction_threshold"`
}

type SemanticAnalysisConfig struct {
	NLPModelPath       string `yaml:"nlp_model_path"`
	CodeAnalysisRules  string `yaml:"code_analysis_rules"`
	EmbeddingDimension int    `yaml:"embedding_dimension"`
}

type MemoryNetworkConfig struct {
	MemorySize     int    `yaml:"memory_size"`
	ReadHeads      int    `yaml:"read_heads"`
	ControllerType string `yaml:"controller_type"`
}

type QuantumDetectionConfig struct {
	QuantumCircuits  int    `yaml:"quantum_circuits"`
	MeasurementBasis string `yaml:"measurement_basis"`
}

type KnowledgeGraphConfig struct {
	OntologyPath    string `yaml:"ontology_path"`
	ReasoningEngine string `yaml:"reasoning_engine"`
}

type OnlineLearningConfig struct {
	AdaptationRate   float64 `yaml:"adaptation_rate"`
	ForgettingFactor float64 `yaml:"forgetting_factor"`
	BufferSize       int     `yaml:"buffer_size"`
}

type AdaptiveThresholdConfig struct {
	BaseThreshold      float64 `yaml:"base_threshold"`
	AdaptationRate     float64 `yaml:"adaptation_rate"`
	ConfidenceInterval float64 `yaml:"confidence_interval"`
}

// Missing method implementations for ZeroDayDetector
func (s *SemanticAnalyzer) AnalyzeSemantics(ctx context.Context, input *InputVector) (map[string]float64, error) {
	return map[string]float64{}, nil
}

func (z *ZeroDayDetector) formatSemanticEvidence(features map[string]float64) []string {
	return []string{}
}

func (z *ZeroDayDetector) calculateSemanticConfidence(features map[string]float64) float64 {
	return 0.5
}

func (m *MemoryAugmentedNetwork) ProcessInput(ctx context.Context, input *InputVector) (*MemoryActivation, error) {
	return &MemoryActivation{
		NoveltyScore:      0.5,
		ActivatedMemories: []MemorySlot{},
	}, nil
}

func (q *QuantumAnomalyDetector) AnalyzeQuantumSignature(ctx context.Context, input *InputVector) (*QuantumSignature, error) {
	return &QuantumSignature{}, nil
}

func (k *CyberSecurityKnowledgeGraph) ReasonAboutThreat(ctx context.Context, input *InputVector, analysis *ZeroDayAnalysis) (*KnowledgeMatch, error) {
	return &KnowledgeMatch{
		InferredThreats: []string{},
		ConfidenceScore: 0.5,
	}, nil
}

func (z *ZeroDayDetector) runEnsembleModels(input *InputVector) []*EnsembleResult {
	return []*EnsembleResult{}
}

func (z *ZeroDayDetector) calculateEnsembleConfidence(results []*EnsembleResult) float64 {
	return 0.5
}

func (a *AdaptiveThreshold) GetCurrentThreshold(ctx context.Context, input *InputVector) float64 {
	return 0.5
}

func (z *ZeroDayDetector) calculateFinalZeroDayScore(analysis *ZeroDayAnalysis, ensembleConfidence float64) float64 {
	return 0.5
}

func (z *ZeroDayDetector) enrichWithThreatIntel(ctx context.Context, analysis *ZeroDayAnalysis) *EnrichedThreatIntel {
	return &EnrichedThreatIntel{}
}

func (o *OnlineLearningSystem) LearnFromDetection(analysis *ZeroDayAnalysis) error {
	return nil
}

func (a *AdaptiveThreshold) UpdateThreshold(score float64, isZeroDay bool) error {
	return nil
}

func (z *ZeroDayDetector) analyzeTemporalPatterns(input *InputVector) []TemporalPattern {
	return []TemporalPattern{}
}

func (z *ZeroDayDetector) analyzeSpatialPatterns(input *InputVector) []SpatialPattern {
	return []SpatialPattern{}
}

func (z *ZeroDayDetector) analyzeFrequencyDomain(input *InputVector) *FrequencyAnalysis {
	return &FrequencyAnalysis{}
}

// Placeholder types for the stub methods
type EnsembleResult struct{}
type ThreatIntelligenceData struct{}
type TemporalFeatures struct{}
type SpatialFeatures struct{}
type FrequencyFeatures struct{}
