Integrations
Sends detailed system monitoring reports to Discord channels through configurable webhooks, with separate reports for hardware metrics and network analysis, formatted as visually optimized embeds
Uses pandas for data manipulation of collected system metrics before analysis and reporting
Built on Python 3.8+ with a CLI interface for running system monitoring, anomaly detection, and reporting commands
MCP_AI_Monitor
🔍 Overview
MCP_AI_Monitor is a comprehensive system monitoring solution that uses unsupervised machine learning algorithms to detect abnormal behavior in resource usage. Designed to provide deep visibility into your system's performance in real time, it combines data collection, predictive analysis, and detailed reporting.
✨ Main Features
- 🤖 AI Anomaly Detection - Uses Isolation Forest to identify unusual system behaviors
- 📊 Real-time Analysis - Continuous monitoring of CPU, RAM and network metrics
- 🧠 Adaptive Learning - Adjusts to your system's normal behavior to reduce false positives
- 📱 Instant Notifications - System alerts when anomalies are detected
- 📈 Detailed Visualizations - Resource usage graphs with trend identification
- ⚙️ Process Analysis - Identification of resource-intensive applications
- 🌐 Network Monitoring - Analyze active connections and network performance
- 📡 Discord Integration - Detailed reports automatically sent to your Discord channels
- 🎨 Modern CLI Interface - Colorful and intuitive display in the terminal
🚀 Available orders
Order | Description |
---|---|
python mcp.py collect | System data collection (CPU, RAM) |
python mcp.py train | Trains AI model for anomaly detection |
python mcp.py monitor | Launches real-time monitoring with anomaly detection |
python mcp.py stats | Generates usage graphs and statistics |
python mcp.py discord | Sends detailed reports to Discord |
python mcp.py network | Analyzes the network and sends a dedicated report |
python mcp.py all | Performs the complete sequence (collection, training, monitoring) |
🛠️ Architecture
MCP_AI_Monitor is composed of several add-ons:
- Data collection module (
collect_data.py
)- Records system metrics at regular intervals
- Stores data in CSV format for later analysis
- AI Training Module (
train_model.py
)- Pre-processes the collected data
- Train an Isolation Forest model for anomaly detection
- Save the model for real-time use
- Monitoring module (
monitor_ai.py
)- Uses the trained model to detect anomalies in real time
- Implements a learning phase to adapt to normal behavior
- Distinguishes application launches from real anomalies
- Discord Integration
- Sends separate reports for hardware and network
- Uses configurable webhooks for each data category
- Optimized visual format with thematic embeds
📊 Discord Reports
MCP_AI_Monitor generates detailed reports and sends them to Discord via dedicated webhooks:
Hardware reports
- System Information - Details about CPU, RAM, OS
- Usage Graphs - Visualize CPU/RAM Trends
- Active Processes - List of the most power-hungry applications
Network reports
- Network activity - Upload/download speeds, data volumes
- Network Interfaces - Details of active interfaces and their IP addresses
- Active Connections - Tracking established connections and associated processes
📋 Prerequisites
- Python 3.8+
- Python dependencies (installable via
pip install -r requirements.txt
):- psutil - System Data Collection
- scikit-learn - Machine learning algorithms
- pandas - Data Manipulation
- matplotlib - Graph generation
- colorama - Colorful display in the terminal
- discord-webhook - Integration with Discord
🔧 Installation
- Clone this repository:
- Install the dependencies:
- Configure your Discord webhooks (optional):
- Change the webhook URLs in the
mcp.py
file - Ability to use separate webhooks for hardware and network reports
- Change the webhook URLs in the
📖 User guide
Quick Start
For a first full use:
Automated workflow
To run the entire process in one command:
🔍 Anomaly detection
The system uses an Isolation Forest algorithm to detect abnormal behavior:
- Learning Phase - Collecting data to establish a baseline
- Dynamic adaptation - Adjusting thresholds based on normal behavior
- Smart Filtering - Detect app launches to reduce false positives
- Anomaly Scoring - Classifying Events by Level of Abnormality
🌱 Contribution
Contributions are welcome! To contribute:
- Fork the project
- Create a branch for your feature (
git checkout -b feature/amazing-feature
) - Commit your changes (
git commit -m 'Add some amazing feature'
) - Push to the branch (
git push origin feature/amazing-feature
) - Open a Pull Request
📜 License
This project is licensed under the MIT License. See the LICENSE
file for more information.
👥 Authors
- MedusaSH - Initial Development - Github
🙏 Acknowledgments
- Forest insulation by scikit-learn
- psutil for accessing system metrics
- discord-webhook library for Discord integration
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An advanced system monitoring solution that uses unsupervised machine learning algorithms to detect abnormal resource usage patterns in real-time, with features including anomaly detection, process analysis, and Discord integration.
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