feat(Production): Complete production deployment infrastructure

- Add comprehensive health check system with multiple endpoints
- Add Prometheus metrics endpoint
- Add production logging configurations (5 strategies)
- Add complete deployment documentation suite:
  * QUICKSTART.md - 30-minute deployment guide
  * DEPLOYMENT_CHECKLIST.md - Printable verification checklist
  * DEPLOYMENT_WORKFLOW.md - Complete deployment lifecycle
  * PRODUCTION_DEPLOYMENT.md - Comprehensive technical reference
  * production-logging.md - Logging configuration guide
  * ANSIBLE_DEPLOYMENT.md - Infrastructure as Code automation
  * README.md - Navigation hub
  * DEPLOYMENT_SUMMARY.md - Executive summary
- Add deployment scripts and automation
- Add DEPLOYMENT_PLAN.md - Concrete plan for immediate deployment
- Update README with production-ready features

All production infrastructure is now complete and ready for deployment.

src/Framework/Waf/MachineLearning/MachineLearningEngine.php

@@ -7,10 +7,14 @@ namespace App\Framework\Waf\MachineLearning;
 use App\Framework\Core\ValueObjects\Duration;
 use App\Framework\Core\ValueObjects\Percentage;
 use App\Framework\DateTime\Clock;
+use App\Framework\MachineLearning\Core\AnomalyDetectorInterface;
+use App\Framework\MachineLearning\Core\FeatureExtractorMetadata;
+use App\Framework\MachineLearning\ValueObjects\AnomalyDetection;
+use App\Framework\MachineLearning\ValueObjects\Baseline;
+use App\Framework\MachineLearning\ValueObjects\Feature;
+use App\Framework\MachineLearning\ValueObjects\FeatureType;
 use App\Framework\Waf\Analysis\ValueObjects\RequestAnalysisData;
-use App\Framework\Waf\MachineLearning\ValueObjects\AnomalyDetection;
-use App\Framework\Waf\MachineLearning\ValueObjects\BehaviorBaseline;
-use App\Framework\Waf\MachineLearning\ValueObjects\BehaviorFeature;
+use App\Framework\Waf\MachineLearning\WafFeatureExtractor;
 
 /**
  * Main machine learning engine for behavioral analysis and anomaly detection
@@ -18,7 +22,7 @@ use App\Framework\Waf\MachineLearning\ValueObjects\BehaviorFeature;
 final class MachineLearningEngine
 {
     /**
-     * @param FeatureExtractorInterface[] $extractors
+     * @param WafFeatureExtractor[] $extractors
      * @param AnomalyDetectorInterface[] $detectors
      */
     public function __construct(
@@ -141,7 +145,7 @@ final class MachineLearningEngine
 
                 $extractorResults[] = [
                     'extractor' => get_class($extractor),
-                    'behavior_type' => $extractor->getBehaviorType()->value,
+                    'feature_type' => $extractor->getFeatureType()->value,
                     'feature_count' => count($validFeatures),
                     'processing_time' => $extractorTime->toMilliseconds(),
                     'success' => true,
@@ -155,7 +159,7 @@ final class MachineLearningEngine
             } catch (\Throwable $e) {
                 $extractorResults[] = [
                     'extractor' => get_class($extractor),
-                    'behavior_type' => $extractor->getBehaviorType()->value,
+                    'feature_type' => $extractor->getFeatureType()->value,
                     'feature_count' => 0,
                     'processing_time' => 0,
                     'success' => false,
@@ -239,10 +243,10 @@ final class MachineLearningEngine
     {
         $baselines = [];
 
-        // Group features by behavior type
+        // Group features by feature type
         $featureGroups = [];
         foreach ($features as $feature) {
-            if ($feature instanceof BehaviorFeature) {
+            if ($feature instanceof Feature) {
                 $typeKey = $feature->type->value;
                 if (! isset($featureGroups[$typeKey])) {
                     $featureGroups[$typeKey] = [];
@@ -251,17 +255,17 @@ final class MachineLearningEngine
             }
         }
 
-        // Get or create baselines for each behavior type
-        foreach ($featureGroups as $behaviorType => $groupFeatures) {
-            $cacheKey = "baseline:{$behaviorType}";
+        // Get or create baselines for each feature type
+        foreach ($featureGroups as $featureType => $groupFeatures) {
+            $cacheKey = "baseline:{$featureType}";
 
             if (isset($this->baselineCache[$cacheKey])) {
-                $baselines[$behaviorType] = $this->baselineCache[$cacheKey];
+                $baselines[$featureType] = $this->baselineCache[$cacheKey];
             } else {
                 // Create new baseline from features
-                $baseline = $this->createBaselineFromFeatures($groupFeatures, BehaviorType::from($behaviorType));
+                $baseline = $this->createBaselineFromFeatures($groupFeatures, FeatureType::from($featureType));
                 if ($baseline !== null) {
-                    $baselines[$behaviorType] = $baseline;
+                    $baselines[$featureType] = $baseline;
                     $this->baselineCache[$cacheKey] = $baseline;
                 }
             }
@@ -273,13 +277,13 @@ final class MachineLearningEngine
     /**
      * Create behavioral baseline from feature set
      */
-    private function createBaselineFromFeatures(array $features, BehaviorType $behaviorType): ?BehaviorBaseline
+    private function createBaselineFromFeatures(array $features, FeatureType $featureType): ?Baseline
     {
         if (empty($features)) {
             return null;
         }
 
-        $values = array_map(fn (BehaviorFeature $f) => $f->value, $features);
+        $values = array_map(fn (Feature $f) => $f->value, $features);
 
         if (empty($values)) {
             return null;
@@ -294,28 +298,36 @@ final class MachineLearningEngine
         $p95 = $values[(int)(count($values) * 0.95)];
         $p99 = $values[(int)(count($values) * 0.99)];
 
-        return new BehaviorBaseline(
-            type: $behaviorType,
+        return new Baseline(
+            type: $featureType,
+            identifier: 'dynamic-baseline',
             mean: $mean,
             standardDeviation: $stdDev,
-            sampleSize: count($values),
-            p50: $p50,
-            p95: $p95,
-            p99: $p99,
-            confidence: Percentage::from(min(100.0, count($values) * 5.0)), // 5% per sample, max 100%
-            lastUpdated: $this->clock->time()
+            median: $p50,
+            minimum: min($values),
+            maximum: max($values),
+            percentiles: [
+                50 => $p50,
+                95 => $p95,
+                99 => $p99
+            ],
+            sampleCount: count($values),
+            createdAt: $this->clock->time(),
+            lastUpdated: $this->clock->time(),
+            windowSize: Duration::fromMinutes(30),
+            confidence: min(1.0, count($values) * 0.05) // 5% per sample, max 100%
         );
     }
 
     /**
      * Get relevant baseline for a detector
      */
-    private function getRelevantBaseline(AnomalyDetectorInterface $detector, array $baselines): ?BehaviorBaseline
+    private function getRelevantBaseline(AnomalyDetectorInterface $detector, array $baselines): ?Baseline
     {
-        $supportedTypes = $detector->getSupportedBehaviorTypes();
+        $supportedTypes = $detector->getSupportedFeatureTypes();
 
-        foreach ($supportedTypes as $behaviorType) {
-            $typeKey = $behaviorType->value;
+        foreach ($supportedTypes as $featureType) {
+            $typeKey = $featureType->value;
             if (isset($baselines[$typeKey])) {
                 return $baselines[$typeKey];
             }
@@ -332,7 +344,7 @@ final class MachineLearningEngine
         $validFeatures = [];
 
         foreach ($features as $feature) {
-            if ($feature instanceof BehaviorFeature) {
+            if ($feature instanceof Feature) {
                 // Validate feature values
                 if (is_numeric($feature->value) &&
                     ! is_nan($feature->value) &&
@@ -530,13 +542,13 @@ final class MachineLearningEngine
      */
     private function deduplicateAndRankAnomalies(array $anomalies): array
     {
-        // Remove duplicates based on type and behavior type
+        // Remove duplicates based on type and feature type
         $seen = [];
         $unique = [];
 
         foreach ($anomalies as $anomaly) {
             if ($anomaly instanceof AnomalyDetection) {
-                $key = $anomaly->type->value . ':' . $anomaly->behaviorType->value;
+                $key = $anomaly->type->value . ':' . $anomaly->featureType->value;
                 if (! isset($seen[$key]) || $anomaly->confidence->getValue() > $seen[$key]->confidence->getValue()) {
                     $seen[$key] = $anomaly;
                 }
@@ -560,8 +572,8 @@ final class MachineLearningEngine
             'path' => $requestData->path,
             'method' => $requestData->method,
             'params' => $requestData->getAllParameters(),
-            'user_agent' => $requestData->userAgent?->toString(),
-            'ip' => $requestData->clientIp?->toString(),
+            'user_agent' => (string) $requestData->userAgent,
+            'ip' => (string) $requestData->clientIp,
         ]));
     }
 
@@ -574,7 +586,7 @@ final class MachineLearningEngine
         $featuresByType = [];
 
         foreach ($features as $feature) {
-            if ($feature instanceof BehaviorFeature) {
+            if ($feature instanceof Feature) {
                 $typeKey = $feature->type->value;
                 if (! isset($featuresByType[$typeKey])) {
                     $featuresByType[$typeKey] = [];
@@ -583,9 +595,9 @@ final class MachineLearningEngine
             }
         }
 
-        foreach ($featuresByType as $behaviorType => $typeFeatures) {
+        foreach ($featuresByType as $featureType => $typeFeatures) {
             $results[] = [
-                'behavior_type' => $behaviorType,
+                'feature_type' => $featureType,
                 'feature_count' => count($typeFeatures),
                 'avg_value' => array_sum(array_map(fn ($f) => $f->value, $typeFeatures)) / count($typeFeatures),
                 'feature_names' => array_unique(array_map(fn ($f) => $f->name, $typeFeatures)),
@@ -632,14 +644,14 @@ final class MachineLearningEngine
     {
         $stats = [];
 
-        foreach ($baselines as $behaviorType => $baseline) {
-            if ($baseline instanceof BehaviorBaseline) {
+        foreach ($baselines as $featureType => $baseline) {
+            if ($baseline instanceof Baseline) {
                 $stats[] = [
-                    'behavior_type' => $behaviorType,
-                    'sample_size' => $baseline->sampleSize,
+                    'feature_type' => $featureType,
+                    'sample_size' => $baseline->sampleCount,
                     'mean' => $baseline->mean,
                     'std_dev' => $baseline->standardDeviation,
-                    'confidence' => $baseline->confidence->getValue(),
+                    'confidence' => $baseline->confidence,
                     'last_updated' => $baseline->lastUpdated->toIso8601String(),
                 ];
             }
@@ -654,7 +666,7 @@ final class MachineLearningEngine
     private function recordPerformanceMetrics(Duration $processingTime, int $featureCount, int $anomalyCount): void
     {
         $this->performanceMetrics[] = [
-            'timestamp' => $this->clock->time()->toUnixTimestamp(),
+            'timestamp' => $this->clock->time()->toTimestamp(),
             'processing_time_ms' => $processingTime->toMilliseconds(),
             'feature_count' => $featureCount,
             'anomaly_count' => $anomalyCount,