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

未指定任何文件、测试或入口点。先定位现有的监控和流量处理路径，然后在实现之前明确模型、训练数据、推理、告警和可视化的边界。满足验收标准即视为完成，包括支持重新训练、较低的误报率、告警以及可视化结果。

由索引模型根据 Issue 内容生成。