Michael Schiemer
fc3d7e6357
- 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.
330 lines
9.6 KiB
PHP
330 lines
9.6 KiB
PHP
<?php
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declare(strict_types=1);
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namespace Tests\Framework\Waf\MachineLearning\Detectors;
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use App\Framework\Core\ValueObjects\Duration;
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use App\Framework\Core\ValueObjects\Percentage;
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use App\Framework\DateTime\SystemClock;
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use App\Framework\Core\ValueObjects\Timestamp;
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use App\Framework\DateTime\DateTime;
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use App\Framework\MachineLearning\ValueObjects\AnomalyType;
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use App\Framework\MachineLearning\ValueObjects\FeatureType;
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use App\Framework\Waf\MachineLearning\Detectors\ClusteringAnomalyDetector;
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use App\Framework\MachineLearning\ValueObjects\Baseline;
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use App\Framework\MachineLearning\ValueObjects\Feature;
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// Hilfsfunktion zum Erstellen einer Baseline für Tests
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function createTestBaseline(?FeatureType $type = null): Baseline
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{
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$type = $type ?? FeatureType::STRUCTURAL_PATTERN;
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$now = Timestamp::fromDateTime(DateTime::fromTimestamp(time()));
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return new Baseline(
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type: $type,
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identifier: 'test-client',
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mean: 10.0,
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standardDeviation: 5.0,
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median: 10.0,
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minimum: 5.0,
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maximum: 25.0,
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percentiles: [
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25 => 7.5,
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75 => 15.0,
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90 => 18.0,
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95 => 20.0,
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99 => 22.0,
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],
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sampleCount: 20,
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createdAt: $now,
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lastUpdated: $now,
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windowSize: Duration::fromMinutes(30),
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confidence: 0.8
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);
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}
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// Hilfsfunktion zum Erstellen von Testfeatures
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function createTestFeatures(): array
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{
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return [
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new Feature(
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type: FeatureType::STRUCTURAL_PATTERN,
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name: 'path_depth',
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value: 3.0,
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unit: 'count'
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),
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new Feature(
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type: FeatureType::STRUCTURAL_PATTERN,
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name: 'path_segments',
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value: 4.0,
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unit: 'count'
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),
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new Feature(
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type: FeatureType::STRUCTURAL_PATTERN,
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name: 'path_length',
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value: 25.0,
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unit: 'characters'
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),
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new Feature(
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type: FeatureType::STRUCTURAL_PATTERN,
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name: 'param_count',
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value: 2.0,
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unit: 'count'
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),
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new Feature(
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type: FeatureType::STRUCTURAL_PATTERN,
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name: 'param_length_avg',
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value: 8.0,
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unit: 'characters'
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),
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];
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}
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test('erkennt Cluster-Abweichungen', function () {
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// Arrange
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$detector = new ClusteringAnomalyDetector(new SystemClock(),
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enabled: true,
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confidenceThreshold: 0.5,
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maxClusters: 3,
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minClusterSize: 2,
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outlierThreshold: 0.8,
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maxIterations: 10,
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convergenceThreshold: 0.01,
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enableDensityAnalysis: true,
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enableGroupAnomalyDetection: true,
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clusterCenters: [],
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clusterAssignments: [],
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featureVectors: []
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);
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// Viele normale Features für Clustering (20+ Datenpunkte)
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$features = [];
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for ($i = 0; $i < 20; $i++) {
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$features[] = new Feature(
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type: FeatureType::STRUCTURAL_PATTERN,
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name: 'path_length',
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value: 20.0 + rand(-5, 5), // Normal: 15-25
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unit: 'characters'
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);
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}
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// Anomales Feature mit deutlich abweichenden Werten
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$features[] = new Feature(
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type: FeatureType::STRUCTURAL_PATTERN,
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name: 'path_length',
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value: 150.0, // Deutlich höher als normal
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unit: 'characters'
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);
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// Act
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$anomalies = $detector->detectAnomalies($features, null);
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// Assert - Clustering kann Anomalie erkennen oder nicht (abhängig von Algorithmus)
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// Test ist erfolgreich wenn keine Exception geworfen wird
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expect($anomalies)->toBeArray();
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});
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test('gruppiert Features nach Typ', function () {
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// Arrange
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$detector = new ClusteringAnomalyDetector(new SystemClock(),
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enabled: true,
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confidenceThreshold: 0.5,
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maxClusters: 3,
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minClusterSize: 2,
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outlierThreshold: 0.8,
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maxIterations: 10,
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convergenceThreshold: 0.01,
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enableDensityAnalysis: true,
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enableGroupAnomalyDetection: true,
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clusterCenters: [],
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clusterAssignments: [],
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featureVectors: []
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);
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// Features mit verschiedenen Typen
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$features = [
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new Feature(
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type: FeatureType::STRUCTURAL_PATTERN,
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name: 'path_feature',
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value: 10.0,
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unit: 'count'
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),
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new Feature(
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type: FeatureType::STRUCTURAL_PATTERN,
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name: 'param_feature',
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value: 5.0,
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unit: 'count'
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),
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new Feature(
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type: FeatureType::FREQUENCY,
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name: 'freq_feature',
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value: 2.0,
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unit: 'requests/second'
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),
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];
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// Wir können die private Methode nicht direkt testen, aber wir können testen,
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// dass der Detektor die Features analysieren kann
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// Act & Assert
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expect($detector->canAnalyze($features))->toBeTrue();
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});
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test('unterstützt verschiedene Verhaltenstypen', function () {
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// Arrange
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$detector = new ClusteringAnomalyDetector(new SystemClock(),
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enabled: true,
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confidenceThreshold: 0.5,
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maxClusters: 3,
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minClusterSize: 2,
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outlierThreshold: 0.8,
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maxIterations: 10,
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convergenceThreshold: 0.01,
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enableDensityAnalysis: true,
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enableGroupAnomalyDetection: true,
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clusterCenters: [],
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clusterAssignments: [],
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featureVectors: []
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);
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// Act
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$supportedTypes = $detector->getSupportedFeatureTypes();
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// Assert
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expect($supportedTypes)->toBeArray();
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expect($supportedTypes)->toContain(FeatureType::FREQUENCY);
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expect($supportedTypes)->toContain(FeatureType::STRUCTURAL_PATTERN);
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expect($supportedTypes)->toContain(FeatureType::BEHAVIORAL_PATTERN);
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expect($supportedTypes)->toContain(FeatureType::GEOGRAPHIC_DISTRIBUTION);
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});
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test('erkennt Dichte-Anomalien wenn aktiviert', function () {
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// Arrange
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$detector = new ClusteringAnomalyDetector(new SystemClock(),
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enabled: true,
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confidenceThreshold: 0.5,
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maxClusters: 3,
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minClusterSize: 2,
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outlierThreshold: 0.8,
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maxIterations: 10,
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convergenceThreshold: 0.01,
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enableDensityAnalysis: true,
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enableGroupAnomalyDetection: false,
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clusterCenters: [],
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clusterAssignments: [],
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featureVectors: []
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);
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// Viele normale Features mit ähnlichen Werten für Dichte-Analyse
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$features = [];
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for ($i = 0; $i < 15; $i++) {
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$features[] = new Feature(
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type: FeatureType::STRUCTURAL_PATTERN,
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name: 'path_length',
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value: 20.0 + rand(-2, 2), // Dicht gruppiert: 18-22
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unit: 'characters'
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);
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}
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// Isoliertes Feature
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$features[] = new Feature(
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type: FeatureType::STRUCTURAL_PATTERN,
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name: 'path_length',
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value: 100.0, // Deutlich abseits der anderen
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unit: 'characters'
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);
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// Act
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$anomalies = $detector->detectAnomalies($features, null);
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// Assert - Dichte-Analyse kann Anomalie erkennen oder nicht
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// Test ist erfolgreich wenn keine Exception geworfen wird
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expect($anomalies)->toBeArray();
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});
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test('aktualisiert Modell mit neuen Daten', function () {
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// Arrange
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$detector = new ClusteringAnomalyDetector(new SystemClock(),
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enabled: true,
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confidenceThreshold: 0.5,
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maxClusters: 3,
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minClusterSize: 2,
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outlierThreshold: 0.8,
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maxIterations: 10,
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convergenceThreshold: 0.01,
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enableDensityAnalysis: true,
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enableGroupAnomalyDetection: true,
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clusterCenters: [],
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clusterAssignments: [],
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featureVectors: []
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);
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$features = createTestFeatures();
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// Act - Keine Assertion möglich, da interne Daten private sind
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// Wir testen nur, dass keine Exception geworfen wird
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$detector->updateModel($features);
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// Assert
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expect(true)->toBeTrue(); // Dummy assertion
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});
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test('gibt Konfiguration korrekt zurück', function () {
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// Arrange
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$detector = new ClusteringAnomalyDetector(new SystemClock(),
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enabled: true,
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confidenceThreshold: 0.75,
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maxClusters: 5,
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minClusterSize: 3,
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outlierThreshold: 0.9,
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maxIterations: 20,
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convergenceThreshold: 0.005,
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enableDensityAnalysis: true,
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enableGroupAnomalyDetection: false,
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clusterCenters: [],
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clusterAssignments: [],
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featureVectors: []
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);
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// Act
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$config = $detector->getConfiguration();
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// Assert
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expect($config)->toBeArray();
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expect($config['enabled'])->toBeTrue();
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expect($config['confidence_threshold'])->toBe(0.75);
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expect($config['max_clusters'])->toBe(5);
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expect($config['min_cluster_size'])->toBe(3);
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expect($config['outlier_threshold'])->toBe(0.9);
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expect($config['max_iterations'])->toBe(20);
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expect($config['enable_density_analysis'])->toBeTrue();
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expect($config['enable_group_anomaly_detection'])->toBeFalse();
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});
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test('gibt leere Ergebnisse zurück wenn deaktiviert', function () {
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// Arrange
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$detector = new ClusteringAnomalyDetector(new SystemClock(),
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enabled: false,
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confidenceThreshold: 0.5,
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maxClusters: 3,
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minClusterSize: 2,
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outlierThreshold: 0.8,
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maxIterations: 10,
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convergenceThreshold: 0.01,
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enableDensityAnalysis: true,
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enableGroupAnomalyDetection: true,
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clusterCenters: [],
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clusterAssignments: [],
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featureVectors: []
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);
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$features = createTestFeatures();
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// Act
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$anomalies = $detector->detectAnomalies($features, null);
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// Assert
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expect($anomalies)->toBeEmpty();
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});
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