🔄 Advanced Training Pipeline
Our ML training pipeline employs cutting-edge techniques for optimal model performance and validation.
1
Data Collection
750K+ high-quality samples with synthetic data augmentation
2
Preprocessing
Feature engineering, normalization, and stratified sampling
3
Model Training
Hyperparameter optimization and ensemble methods
4
Validation
15-fold cross-validation with 99% confidence
5
Deployment
Production deployment with monitoring
// ML Training Infrastructure
import { mlTrainingInfrastructure } from './ml-training-infrastructure.js';
import { mlPerformanceOptimizer } from './ml-performance-optimizer.js';
import { mlValidationEngine } from './ml-validation-engine.js';
// Initialize and execute enhanced training
const results = await mlPerformanceOptimizer.executePerformanceOptimization();
console.log(`Models trained with ${results.overallImprovement}% improvement`);
// Comprehensive validation
const validation = await mlValidationEngine.validateAllModels();
console.log(`${validation.overall.passedValidation}/${validation.overall.modelsValidated} models validated`);
⚡ Advanced ML Techniques
State-of-the-art machine learning techniques employed for maximum performance.
🎯 Hyperparameter Optimization
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Grid search across parameter space
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Bayesian optimization
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Automated learning rate scheduling
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Early stopping with patience
🤝 Ensemble Methods
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5-model ensembles per algorithm
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Weighted voting strategies
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Knowledge distillation
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Model diversity optimization
📊 Data Augmentation
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Synthetic data generation (VAE/GAN)
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Noise injection and mixup
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Contrastive learning
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Curriculum learning strategies
✅ Validation & Testing
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15-fold cross-validation
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Statistical significance testing
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Robustness evaluation
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Adversarial testing
🏗️ Deep Learning Architectures
Advanced neural network architectures optimized for cryptographic applications.
// Deep Learning Architecture Configuration
const architectures = {
'performance-lstm': {
type: 'LSTM',
layers: [
{ type: 'embedding', size: 128 },
{ type: 'lstm', units: 256, return_sequences: true },
{ type: 'lstm', units: 128 },
{ type: 'dense', units: 64, activation: 'relu' },
{ type: 'dropout', rate: 0.3 },
{ type: 'dense', units: 4, activation: 'softmax' }
],
optimizer: 'adam',
learning_rate: 0.001
},
'threat-cnn': {
type: 'CNN',
layers: [
{ type: 'conv1d', filters: 64, kernel_size: 3, activation: 'relu' },
{ type: 'conv1d', filters: 128, kernel_size: 3, activation: 'relu' },
{ type: 'global_max_pooling1d' },
{ type: 'dense', units: 128, activation: 'relu' },
{ type: 'dropout', rate: 0.5 },
{ type: 'dense', units: 6, activation: 'sigmoid' }
],
optimizer: 'rmsprop',
learning_rate: 0.0005
},
'transformer': {
type: 'Transformer',
d_model: 512,
num_heads: 8,
num_layers: 6,
d_ff: 2048,
dropout_rate: 0.1,
max_seq_length: 1024
}
};
✅ Operational ML Stack
Currently deployed machine learning infrastructure powering real-time threat detection, performance prediction, and intelligent algorithm selection.
🐍 PyTorch Neural Networks
Production Ready
Deep learning models for threat detection and performance prediction using PyTorch framework.
Use Cases
Threat Analysis
📊 Scikit-learn Models
Production Ready
Traditional machine learning models for classification, regression, and clustering tasks.
Algorithms
RF, SVM, XGBoost
🦀 Rust-Python Bridge (PyO3)
Production Ready
High-performance Rust API server with Python ML service integration via PyO3 bindings.
// Operational ML Service Integration (Rust + Python)
// Located in: /var/www/html/public/ent/api/src/services/ml_service.rs
pub struct MLService {
python_runtime: Arc<Mutex<PythonRuntime>>,
model_cache: Arc<RwLock<HashMap<String, MLModel>>>,
metrics: Arc<MLMetrics>,
}
impl MLService {
// Threat detection with PyTorch neural networks
pub async fn analyze_threat(&self, request: ThreatAnalysisRequest)
-> Result<ThreatAnalysisResult>;
// Performance prediction with scikit-learn
pub async fn predict_performance(&self, request: PerformancePredictionRequest)
-> Result<PerformancePredictionResult>;
// Algorithm selection with Random Forest
pub async fn select_algorithm(&self, request: AlgorithmSelectionRequest)
-> Result<AlgorithmSelectionResult>;
}
📊 Training Datasets
Production-grade datasets with comprehensive validation pipelines for ML training.
🎯 Algorithm Selection Dataset
20-feature dataset for intelligent cryptographic algorithm selection.
🗜️ Compression Predictor Dataset
Multi-output regression dataset for compression performance prediction.
🛡️ Threat Assessment Dataset
Security-focused dataset for threat detection and risk assessment.
// Dataset generation example
const datasetGenerator = new TrainingDatasets();
await datasetGenerator.init();
const algorithmDataset = await datasetGenerator.generateDataset('algorithm-selector', {
samples: 10000,
features: ['data_size', 'entropy', 'security_level', 'performance_requirement'],
labels: ['classical', 'pq', 'hybrid', 'multi-pq', 'max-secure-pqc'],
validationSplit: 0.2
});
console.log(`Generated ${algorithmDataset.samples.length} training samples`);
🗣️ LLM Integration
Local Large Language Model capabilities using Transformers.js for intelligent cryptographic configuration.
🔍 Threat Analyzer
DistilBERT-based text classification for security threat analysis and sentiment scoring.
📝 Policy Generator
GPT-2 based text generation for automated security policy creation and documentation.
❓ Question Answerer
DistilBERT QA model for cryptographic documentation queries and intelligent assistance.
// LLM Integration example
import { llmIntegration } from './llm-integration.js';
// Threat analysis
const threatAnalysis = await llmIntegration.analyzeThreat({
text: 'Suspicious network activity detected',
context: 'network-traffic'
});
console.log(`Threat level: ${threatAnalysis.classification}`);
// Policy generation
const policy = await llmIntegration.generatePolicy({
requirements: 'high security encryption with quantum resistance',
template: 'enterprise'
});
console.log(`Generated policy: ${policy.text}`);
// Question answering
const answer = await llmIntegration.answerQuestion({
question: 'What key length is recommended for RSA in 2024?',
context: cryptographicDocumentation
});
console.log(`Answer: ${answer.text}`);
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