Machine Learning and Cybersecurity

6.

Model Development and Deployment

6.1.

Model Selection and Design

6.1.1.

Problem Formulation

6.1.1.1.

Defining Objectives

6.1.1.2.

Success Metrics

6.1.1.3.

Constraints and Requirements

6.1.2.

Algorithm Selection

6.1.2.1.

Performance Considerations

6.1.2.2.

Interpretability Requirements

6.1.2.3.

Computational Constraints

6.1.2.4.

Data Characteristics

6.1.3.

Model Architecture Design

6.1.3.1.

Feature Engineering

6.1.3.2.

Model Complexity

6.1.3.3.

Hyperparameter Selection

6.1.4.

Evaluation Methodology

6.1.4.1.

Cross-Validation Strategies

6.1.4.2.

Performance Metrics

6.1.4.2.1.

Classification Metrics

6.1.4.2.1.1.

6.1.4.2.1.2.

6.1.4.2.1.3.

6.1.4.2.1.4.

6.1.4.2.1.5.

Matthews Correlation Coefficient

6.1.4.2.1.6.

6.1.4.2.1.7.

Precision-Recall AUC

6.1.4.2.2.

Regression Metrics

6.1.4.2.3.

Ranking Metrics

6.1.4.3.

Statistical Significance Testing

6.2.

Model Training and Optimization

6.2.1.

Training Strategies

6.2.1.1.

6.2.1.2.

Online Learning

6.2.1.3.

Transfer Learning

6.2.1.4.

Multi-Task Learning

6.2.2.

Hyperparameter Optimization

6.2.2.1.

6.2.2.2.

6.2.2.3.

Bayesian Optimization

6.2.2.4.

Evolutionary Algorithms

6.2.3.

Regularization Techniques

6.2.3.1.

L1/L2 Regularization

6.2.3.2.

6.2.3.3.

6.2.3.4.

Data Augmentation

6.2.4.

Handling Class Imbalance

6.2.4.1.

Sampling Techniques

6.2.4.2.

Cost-Sensitive Learning

6.2.4.3.

Threshold Optimization

6.3.

Model Deployment and Integration

6.3.1.

Deployment Architectures

6.3.1.1.

Batch Processing

6.3.1.2.

Real-Time Processing

6.3.1.3.

Stream Processing

6.3.1.4.

6.3.2.

Integration with Security Infrastructure

6.3.2.1.

SIEM Integration

6.3.2.2.

SOAR Integration

6.3.2.3.

API Development

6.3.2.4.

Microservices Architecture

6.3.3.

Scalability Considerations

6.3.3.1.

Horizontal Scaling

6.3.3.2.

Vertical Scaling

6.3.3.3.

6.3.3.4.

Caching Strategies

6.3.4.

Performance Optimization

6.3.4.1.

Model Compression

6.3.4.2.

6.3.4.3.

6.3.4.4.

Knowledge Distillation

6.4.

Model Monitoring and Maintenance

6.4.1.

Performance Monitoring

6.4.1.1.

Real-Time Metrics

6.4.1.2.

Alerting Systems

6.4.1.3.

Dashboard Development

6.4.1.4.

Anomaly Detection in Model Performance

6.4.2.

Data Drift Detection

6.4.2.1.

Statistical Tests

6.4.2.2.

Distribution Comparison

6.4.2.3.

Feature Drift Monitoring

6.4.3.

Concept Drift Detection

6.4.3.1.

Performance-Based Detection

6.4.3.2.

Distribution-Based Detection

6.4.3.3.

Ensemble-Based Detection

6.4.4.

Model Updating Strategies

6.4.4.1.

Retraining Schedules

6.4.4.2.

Incremental Learning

6.4.4.3.

Online Learning

6.4.4.4.

A/B Testing for Model Updates

6.4.5.

Version Control and Rollback

6.4.5.1.

Model Versioning

6.4.5.2.

Experiment Tracking

6.4.5.3.

Rollback Procedures

6.4.5.4.

Canary Deployments

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7. Explainable AI and Interpretability