Implement Machine Learning-Based Anomaly Detection

## 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

No files, tests, or entry points are named. Start by locating the existing monitoring and traffic-handling paths, then clarify the model, training data, inference, alerting, and visualization boundaries before implementation. Done means the acceptance criteria are met, including retraining support, low false positives, alerts, and visualized results.

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