Implement Machine Learning-Based Anomaly Detection

[adhit-r]

Overview

Add ML-based anomaly detection to identify unusual patterns in MCP server behavior and traffic.

Business Value

ML anomaly detection enables proactive threat detection and identifies issues before they become critical.

Current State

• Basic monitoring exists
• No ML components
• No anomaly detection

Subtasks

• Design anomaly detection model
• Collect training data
• Train initial model
• Implement model inference
• Add model retraining pipeline
• Create anomaly alerting
• Build anomaly visualization

Implementation Steps

1. Design model architecture
2. Collect and label data
3. Train model
4. Integrate inference
5. Add alerting

Acceptance Criteria

• Anomalies are detected
• False positive rate is low
• Model can be retrained
• Anomalies trigger alerts
• Results are visualized

Estimated Effort

• Hours: 40-60
• Complexity: High