[{"_1":2,"_363":-5,"_364":-5},"loaderData",{"_3":4,"_29":30,"_262":263},"root",{"_5":6,"_14":15},"toasts",[7],{"_8":9,"_10":11,"_12":13},"uid","0199a9a7-9061-7742-a867-0ec6b53325a6","title","This MCP server does not support inspection","type","error","visitor",{"_16":17,"_18":19},"viewportWidth",1280,"visitorSession",{"_20":21,"_23":24,"_25":-5,"_26":27,"_28":-5},"attributes",[22],"bot","id",17376273,"membership","referenceId","0199a1cf-fc95-73df-b48c-1059b28acf51","userAccount","routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_31":32,"_78":-5,"_79":33,"_34":80,"_180":181,"_261":167},"availableLocales",[33,45,52,57,64,71],{"_34":35,"_38":42,"_43":44},"mcpServer",{"_36":37,"_38":39,"_40":41},"description","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.","name","MCP AI Monitor","readme","# MCP\\_AI\\_Monitor\n\n![MCP AI Monitor](https://img.shields.io/badge/MCP-AI%20Monitor-00c7ff?style=for-the-badge\\&logo=python\\&logoColor=white)![Python 3.8+](https://img.shields.io/badge/Python-3.8+-blue.svg?style=flat\\&logo=python\\&logoColor=white)![MIT License](https://img.shields.io/badge/License-MIT-green.svg?style=flat)![Version 1.0.0](https://img.shields.io/badge/Version-1.0.0-orange.svg?style=flat)\n\n## 🔍 Overview\n\nMCP\\_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.\n\n## ✨ Main Features\n\n* **🤖 AI Anomaly Detection** - Uses Isolation Forest to identify unusual system behaviors\n* **📊 Real-time Analysis** - Continuous monitoring of CPU, RAM and network metrics\n* **🧠 Adaptive Learning** - Adjusts to your system's normal behavior to reduce false positives\n* **📱 Instant Notifications** - System alerts when anomalies are detected\n* **📈 Detailed Visualizations** - Resource usage graphs with trend identification\n* **⚙️ Process Analysis** - Identification of resource-intensive applications\n* **🌐 Network Monitoring** - Analyze active connections and network performance\n* **📡 Discord Integration** - Detailed reports automatically sent to your Discord channels\n* **🎨 Modern CLI Interface** - Colorful and intuitive display in the terminal\n\n## 🚀 Available orders\n\n| Order | Description |\n| ----------------------- | ----------------------------------------------------------------- |\n| `python mcp.py collect` | System data collection (CPU, RAM) |\n| `python mcp.py train` | Trains AI model for anomaly detection |\n| `python mcp.py monitor` | Launches real-time monitoring with anomaly detection |\n| `python mcp.py stats` | Generates usage graphs and statistics |\n| `python mcp.py discord` | Sends detailed reports to Discord |\n| `python mcp.py network` | Analyzes the network and sends a dedicated report |\n| `python mcp.py all` | Performs the complete sequence (collection, training, monitoring) |\n\n## 🛠️ Architecture\n\nMCP\\_AI\\_Monitor is composed of several add-ons:\n\n1. **Data collection module** ( `collect_data.py` )\n\n * Records system metrics at regular intervals\n * Stores data in CSV format for later analysis\n\n2. **AI Training Module** ( `train_model.py` )\n\n * Pre-processes the collected data\n * Train an Isolation Forest model for anomaly detection\n * Save the model for real-time use\n\n3. **Monitoring module** ( `monitor_ai.py` )\n\n * Uses the trained model to detect anomalies in real time\n * Implements a learning phase to adapt to normal behavior\n * Distinguishes application launches from real anomalies\n\n4. **Discord Integration**\n\n * Sends separate reports for hardware and network\n * Uses configurable webhooks for each data category\n * Optimized visual format with thematic embeds\n\n## 📊 Discord Reports\n\nMCP\\_AI\\_Monitor generates detailed reports and sends them to Discord via dedicated webhooks:\n\n### Hardware reports\n\n* **System Information** - Details about CPU, RAM, OS\n* **Usage Graphs** - Visualize CPU/RAM Trends\n* **Active Processes** - List of the most power-hungry applications\n\n### Network reports\n\n* **Network activity** - Upload/download speeds, data volumes\n* **Network Interfaces** - Details of active interfaces and their IP addresses\n* **Active Connections** - Tracking established connections and associated processes\n\n## 📋 Prerequisites\n\n* Python 3.8+\n* Python dependencies (installable via `pip install -r requirements.txt` ):\n * psutil - System Data Collection\n * scikit-learn - Machine learning algorithms\n * pandas - Data Manipulation\n * matplotlib - Graph generation\n * colorama - Colorful display in the terminal\n * discord-webhook - Integration with Discord\n\n## 🔧 Installation\n\n1. Clone this repository:\n\n```bash\ngit clone https://github.com/MedusaSH/MCP_AI_Monitor.git\ncd MCP_AI_Monitor\n```\n\n2. Install the dependencies:\n\n```bash\npip install -r requirements.txt\n```\n\n3. Configure your Discord webhooks (optional):\n * Change the webhook URLs in the `mcp.py` file\n * Ability to use separate webhooks for hardware and network reports\n\n## 📖 User guide\n\n### Quick Start\n\nFor a first full use:\n\n```bash\n# Collecte de données (60 secondes par défaut)\npython mcp.py collect\n\n# Entraînement du modèle IA\npython mcp.py train\n\n# Surveillance en temps réel\npython mcp.py monitor\n```\n\n### Automated workflow\n\nTo run the entire process in one command:\n\n```bash\n# Exécute la séquence complète et envoie un rapport sur Discord\npython mcp.py all --duration 120 --report\n```\n\n## 🔍 Anomaly detection\n\nThe system uses an Isolation Forest algorithm to detect abnormal behavior:\n\n1. **Learning Phase** - Collecting data to establish a baseline\n2. **Dynamic adaptation** - Adjusting thresholds based on normal behavior\n3. **Smart Filtering** - Detect app launches to reduce false positives\n4. **Anomaly Scoring** - Classifying Events by Level of Abnormality\n\n## 🌱 Contribution\n\nContributions are welcome! To contribute:\n\n1. Fork the project\n2. Create a branch for your feature ( `git checkout -b feature/amazing-feature` )\n3. Commit your changes ( `git commit -m 'Add some amazing feature'` )\n4. Push to the branch ( `git push origin feature/amazing-feature` )\n5. Open a Pull Request\n\n## 📜 License\n\nThis project is licensed under the MIT License. See the `LICENSE` file for more information.\n\n## 👥 Authors\n\n* **MedusaSH** - *Initial Development* - [Github](https://x.com/MedusaActus)\n\n## 🙏 Acknowledgments\n\n* Forest insulation by scikit-learn\n* psutil for accessing system metrics\n* discord-webhook library for Discord integration\n\n***","English","tag","en-US",{"_34":46,"_38":50,"_43":51},{"_36":47,"_38":48,"_40":49},"Una solución avanzada de monitoreo de sistemas que utiliza algoritmos de aprendizaje automático no supervisados para detectar patrones anormales de uso de recursos en tiempo real, con características que incluyen detección de anomalías, análisis de procesos e integración de Discord.","Monitor de IA MCP","# Monitor de IA MCP\n\n![Monitor de IA MCP](https://img.shields.io/badge/MCP-AI%20Monitor-00c7ff?style=for-the-badge\\&logo=python\\&logoColor=white)![Python 3.8+](https://img.shields.io/badge/Python-3.8+-blue.svg?style=flat\\&logo=python\\&logoColor=white)![Licencia MIT](https://img.shields.io/badge/License-MIT-green.svg?style=flat)![Versión 1.0.0](https://img.shields.io/badge/Version-1.0.0-orange.svg?style=flat)\n\n## 🔍 Descripción general\n\nMCP\\_AI\\_Monitor es una solución integral de monitoreo de sistemas que utiliza algoritmos de aprendizaje automático no supervisados para detectar comportamientos anormales en el uso de recursos. Diseñado para proporcionar una visibilidad profunda del rendimiento de su sistema en tiempo real, combina recopilación de datos, análisis predictivo e informes detallados.\n\n## ✨ Características principales\n\n* **🤖 Detección de anomalías con IA** : utiliza el bosque de aislamiento para identificar comportamientos inusuales del sistema\n* **📊 Análisis en tiempo real** : monitoreo continuo de métricas de CPU, RAM y red\n* **🧠 Aprendizaje adaptativo** : se ajusta al comportamiento normal de su sistema para reducir los falsos positivos.\n* **📱 Notificaciones instantáneas** : el sistema alerta cuando se detectan anomalías\n* **📈 Visualizaciones detalladas** : gráficos de uso de recursos con identificación de tendencias\n* **⚙️ Análisis de procesos** : identificación de aplicaciones que consumen muchos recursos\n* **🌐 Monitoreo de red** : analice las conexiones activas y el rendimiento de la red\n* **📡 Integración con Discord** : informes detallados enviados automáticamente a tus canales de Discord\n* **🎨 Interfaz CLI moderna** : visualización colorida e intuitiva en la terminal\n\n## 🚀 Pedidos disponibles\n\n| Orden | Descripción |\n| ----------------------- | --------------------------------------------------------------------- |\n| `python mcp.py collect` | Recopilación de datos del sistema (CPU, RAM) |\n| `python mcp.py train` | Entrena el modelo de IA para la detección de anomalías |\n| `python mcp.py monitor` | Lanzamiento de monitoreo en tiempo real con detección de anomalías |\n| `python mcp.py stats` | Genera gráficos y estadísticas de uso. |\n| `python mcp.py discord` | Envía informes detallados a Discord |\n| `python mcp.py network` | Analiza la red y envía un informe dedicado. |\n| `python mcp.py all` | Realiza la secuencia completa (recolección, entrenamiento, monitoreo) |\n\n## 🛠️ Arquitectura\n\nMCP\\_AI\\_Monitor se compone de varios complementos:\n\n1. **Módulo de recopilación de datos** ( `collect_data.py` )\n\n * Registra las métricas del sistema a intervalos regulares\n * Almacena datos en formato CSV para su posterior análisis.\n\n2. **Módulo de entrenamiento de IA** ( `train_model.py` )\n\n * Preprocesa los datos recopilados\n * Entrenar un modelo de bosque de aislamiento para la detección de anomalías\n * Guarde el modelo para usarlo en tiempo real\n\n3. **Módulo de monitorización** ( `monitor_ai.py` )\n\n * Utiliza el modelo entrenado para detectar anomalías en tiempo real\n * Implementa una fase de aprendizaje para adaptarse al comportamiento normal.\n * Distingue los lanzamientos de aplicaciones de las anomalías reales\n\n4. **Integración de Discord**\n\n * Envía informes separados para hardware y red\n * Utiliza webhooks configurables para cada categoría de datos\n * Formato visual optimizado con incrustaciones temáticas\n\n## 📊 Informes de Discord\n\nMCP\\_AI\\_Monitor genera informes detallados y los envía a Discord a través de webhooks dedicados:\n\n### Informes de hardware\n\n* **Información del sistema** : detalles sobre CPU, RAM y SO\n* **Gráficos de uso** : visualice las tendencias de CPU/RAM\n* **Procesos activos** : lista de las aplicaciones que consumen más energía\n\n### Informes de red\n\n* **Actividad de la red** : velocidades de carga y descarga, volúmenes de datos\n* **Interfaces de red** : detalles de las interfaces activas y sus direcciones IP\n* **Conexiones activas** : seguimiento de las conexiones establecidas y los procesos asociados\n\n## 📋 Requisitos previos\n\n* Python 3.8+\n* Dependencias de Python (instalables mediante `pip install -r requirements.txt` ):\n * psutil - Recopilación de datos del sistema\n * scikit-learn - Algoritmos de aprendizaje automático\n * pandas - Manipulación de datos\n * matplotlib - Generación de gráficos\n * colorama - Pantalla colorida en la terminal\n * discord-webhook - Integración con Discord\n\n## 🔧 Instalación\n\n1. Clonar este repositorio:\n\n```bash\ngit clone https://github.com/MedusaSH/MCP_AI_Monitor.git\ncd MCP_AI_Monitor\n```\n\n2. Instalar las dependencias:\n\n```bash\npip install -r requirements.txt\n```\n\n3. Configura tus webhooks de Discord (opcional):\n * Cambiar las URL del webhook en el archivo `mcp.py`\n * Capacidad de utilizar webhooks separados para informes de hardware y red\n\n## 📖 Guía del usuario\n\n### Inicio rápido\n\nPara un primer uso completo:\n\n```bash\n# Collecte de données (60 secondes par défaut)\npython mcp.py collect\n\n# Entraînement du modèle IA\npython mcp.py train\n\n# Surveillance en temps réel\npython mcp.py monitor\n```\n\n### Flujo de trabajo automatizado\n\nPara ejecutar todo el proceso en un solo comando:\n\n```bash\n# Exécute la séquence complète et envoie un rapport sur Discord\npython mcp.py all --duration 120 --report\n```\n\n## 🔍 Detección de anomalías\n\nEl sistema utiliza un algoritmo de bosque de aislamiento para detectar comportamientos anormales:\n\n1. **Fase de aprendizaje** : recopilación de datos para establecer una línea base\n2. **Adaptación dinámica** : ajuste de los umbrales en función del comportamiento normal\n3. **Filtrado inteligente** : detecta el inicio de aplicaciones para reducir los falsos positivos\n4. **Puntuación de anomalías** : clasificación de eventos según el nivel de anomalía\n\n## 🌱 Contribución\n\n¡Las contribuciones son bienvenidas! Para contribuir:\n\n1. Bifurcar el proyecto\n2. Crea una rama para tu función ( `git checkout -b feature/amazing-feature` )\n3. Confirme sus cambios ( `git commit -m 'Add some amazing feature'` )\n4. Empujar a la rama ( `git push origin feature/amazing-feature` )\n5. Abrir una solicitud de extracción\n\n## 📜 Licencia\n\nEste proyecto está licenciado bajo la licencia MIT. Consulte el archivo `LICENSE` para obtener más información.\n\n## 👥 Autores\n\n* **MedusaSH** - *Desarrollo inicial* - [Github](https://x.com/MedusaActus)\n\n## 🙏 Agradecimientos\n\n* Aislamiento forestal por scikit-learn\n* psutil para acceder a las métricas del sistema\n* Biblioteca de webhooks de Discord para la integración con Discord\n\n***","Spanish","es-ES",{"_34":53,"_38":55,"_43":56},{"_36":37,"_38":39,"_40":54},"# MCP_AI_Monitor\n\n

\n \n![MCP AI Monitor](https://img.shields.io/badge/MCP-AI%20Monitor-00c7ff?style=for-the-badge&logo=python&logoColor=white)\n![Python 3.8+](https://img.shields.io/badge/Python-3.8+-blue.svg?style=flat&logo=python&logoColor=white)\n![License MIT](https://img.shields.io/badge/License-MIT-green.svg?style=flat)\n![Version 1.0.0](https://img.shields.io/badge/Version-1.0.0-orange.svg?style=flat)\n\n

\n\n

Système de surveillance système avancé avec détection d'anomalies par Intelligence Artificielle

\n\n## 🔍 Vue d'ensemble\n\nMCP_AI_Monitor est une solution complète de surveillance système qui utilise des algorithmes d'apprentissage automatique non supervisés pour détecter des comportements anormaux dans l'utilisation des ressources. Conçu pour offrir une visibilité approfondie sur les performances de votre système en temps réel, il combine collecte de données, analyse prédictive et rapports détaillés.\n\n

\n $\"MCP$ \n

\n\n## ✨ Caractéristiques principales\n\n- **🤖 Détection d'anomalies par IA** - Utilise Isolation Forest pour identifier les comportements système inhabituels\n- **📊 Analyse en temps réel** - Surveillance continue des métriques CPU, RAM et réseau\n- **🧠 Apprentissage adaptatif** - S'ajuste au comportement normal de votre système pour réduire les faux positifs\n- **📱 Notifications instantanées** - Alertes système en cas d'anomalies détectées\n- **📈 Visualisations détaillées** - Graphiques d'utilisation des ressources avec identification des tendances\n- **⚙️ Analyse des processus** - Identification des applications gourmandes en ressources\n- **🌐 Surveillance réseau** - Analyse des connexions actives et des performances réseau\n- **📡 Intégration Discord** - Rapports détaillés automatiquement envoyés sur vos canaux Discord\n- **🎨 Interface CLI moderne** - Affichage coloré et intuitif dans le terminal\n\n## 🚀 Commandes disponibles\n\n| Commande | Description |\n|----------|-------------|\n| `python mcp.py collect` | Collecte des données système (CPU, RAM) |\n| `python mcp.py train` | Entraîne le modèle d'IA pour la détection d'anomalies |\n| `python mcp.py monitor` | Lance la surveillance en temps réel avec détection d'anomalies |\n| `python mcp.py stats` | Génère des graphiques et statistiques d'utilisation |\n| `python mcp.py discord` | Envoie des rapports détaillés sur Discord |\n| `python mcp.py network` | Analyse le réseau et envoie un rapport dédié |\n| `python mcp.py all` | Exécute la séquence complète (collecte, entraînement, surveillance) |\n\n## 🛠️ Architecture\n\nMCP_AI_Monitor est composé de plusieurs modules complémentaires :\n\n1. **Module de collecte de données** (`collect_data.py`)\n - Enregistre les métriques système à intervalles réguliers\n - Stocke les données dans un format CSV pour analyse ultérieure\n\n2. **Module d'entraînement IA** (`train_model.py`)\n - Prétraite les données collectées\n - Entraîne un modèle Isolation Forest pour la détection d'anomalies\n - Sauvegarde le modèle pour une utilisation en temps réel\n\n3. **Module de surveillance** (`monitor_ai.py`)\n - Utilise le modèle entraîné pour détecter les anomalies en temps réel\n - Implémente une phase d'apprentissage pour s'adapter au comportement normal\n - Distingue les lancements d'applications des véritables anomalies\n\n4. **Intégration Discord**\n - Envoie des rapports distincts pour le matériel et le réseau\n - Utilise des webhooks configurables pour chaque catégorie de données\n - Format visuel optimisé avec embeds thématiques\n\n## 📊 Rapports Discord\n\nMCP_AI_Monitor génère des rapports détaillés et les envoie sur Discord via des webhooks dédiés :\n\n### Rapports matériel\n- **Informations système** - Détails sur le CPU, RAM, OS\n- **Graphiques d'utilisation** - Visualisation des tendances CPU/RAM\n- **Processus actifs** - Liste des applications les plus gourmandes\n\n### Rapports réseau\n- **Activité réseau** - Débits montants/descendants, volumes de données\n- **Interfaces réseau** - Détails sur les interfaces actives et leurs adresses IP\n- **Connexions actives** - Suivi des connexions établies et des processus associés\n\n## 📋 Prérequis\n\n- Python 3.8+\n- Dépendances Python (installables via `pip install -r requirements.txt`) :\n - psutil - Collecte de données système\n - scikit-learn - Algorithmes d'apprentissage automatique\n - pandas - Manipulation des données\n - matplotlib - Génération de graphiques\n - colorama - Affichage coloré dans le terminal\n - discord-webhook - Intégration avec Discord\n\n## 🔧 Installation\n\n1. Clonez ce dépôt :\n```bash\ngit clone https://github.com/MedusaSH/MCP_AI_Monitor.git\ncd MCP_AI_Monitor\n```\n\n2. Installez les dépendances :\n```bash\npip install -r requirements.txt\n```\n\n3. Configurez vos webhooks Discord (optionnel) :\n - Modifiez les URLs des webhooks dans le fichier `mcp.py`\n - Possibilité d'utiliser des webhooks distincts pour les rapports matériel et réseau\n\n## 📖 Guide d'utilisation\n\n### Démarrage rapide\n\nPour une première utilisation complète :\n\n```bash\n# Collecte de données (60 secondes par défaut)\npython mcp.py collect\n\n# Entraînement du modèle IA\npython mcp.py train\n\n# Surveillance en temps réel\npython mcp.py monitor\n```\n\n### Workflow automatisé\n\nPour exécuter l'ensemble du processus en une seule commande :\n\n```bash\n# Exécute la séquence complète et envoie un rapport sur Discord\npython mcp.py all --duration 120 --report\n```\n\n## 🔍 Détection d'anomalies\n\nLe système utilise un algorithme d'Isolation Forest pour détecter les comportements anormaux :\n\n1. **Phase d'apprentissage** - Collecte des données pour établir une ligne de base\n2. **Adaptation dynamique** - Ajustement des seuils en fonction du comportement normal\n3. **Filtrage intelligent** - Détection des lancements d'applications pour réduire les faux positifs\n4. **Scoring des anomalies** - Classification des événements par niveau d'anormalité\n\n## 🌱 Contribution\n\nLes contributions sont les bienvenues ! Pour contribuer :\n\n1. Forkez le projet\n2. Créez une branche pour votre fonctionnalité (`git checkout -b feature/amazing-feature`)\n3. Committez vos changements (`git commit -m 'Add some amazing feature'`)\n4. Poussez vers la branche (`git push origin feature/amazing-feature`)\n5. Ouvrez une Pull Request\n\n## 📜 Licence\n\nCe projet est sous licence MIT. Voir le fichier `LICENSE` pour plus d'informations.\n\n## 👥 Auteurs\n\n- **MedusaSH** - *Développement initial* - [Github](https://x.com/MedusaActus)\n\n## 🙏 Remerciements\n\n- Isolation Forest par scikit-learn\n- psutil pour l'accès aux métriques système\n- Bibliothèque discord-webhook pour l'intégration Discord\n\n---\n\n

Développé avec ❤️ et Passion

\n","French","fr-FR",{"_34":58,"_38":62,"_43":63},{"_36":59,"_38":60,"_40":61},"異常検出、プロセス分析、Discord 統合などの機能を備え、教師なし機械学習アルゴリズムを使用して異常なリソース使用パターンをリアルタイムで検出する高度なシステム監視ソリューションです。","MCP AIモニター","# MCP\\_AI\\_モニター\n\n![MCP AIモニター](https://img.shields.io/badge/MCP-AI%20Monitor-00c7ff?style=for-the-badge\\&logo=python\\&logoColor=white)![Python 3.8以上](https://img.shields.io/badge/Python-3.8+-blue.svg?style=flat\\&logo=python\\&logoColor=white)![MITライセンス](https://img.shields.io/badge/License-MIT-green.svg?style=flat)![バージョン1.0.0](https://img.shields.io/badge/Version-1.0.0-orange.svg?style=flat)\n\n## 🔍 概要\n\nMCP\\_AI\\_Monitor は、教師なし機械学習アルゴリズムを使用してリソース使用における異常な動作を検出する包括的なシステム監視ソリューションです。システムのパフォーマンスをリアルタイムで詳細に把握できるように設計されており、データ収集、予測分析、詳細なレポートを組み合わせています。\n\n## ✨ 主な特徴\n\n* **🤖 AI異常検出**- 分離フォレストを使用して異常なシステム動作を識別します\n* **📊 リアルタイム分析**- CPU、RAM、ネットワークメトリックの継続的な監視\n* **🧠 適応学習**- システムの通常の動作に合わせて調整し、誤検知を減らします\n* **📱 即時通知**- 異常が検出されるとシステムが警告します\n* **📈 詳細な視覚化**- 傾向識別機能を備えたリソース使用状況グラフ\n* **⚙️ プロセス分析**- リソースを大量に消費するアプリケーションの識別\n* **🌐 ネットワーク監視**- アクティブな接続とネットワークパフォーマンスを分析\n* **📡 Discord 統合**- 詳細なレポートが Discord チャンネルに自動的に送信されます\n* **🎨 モダンな CLI インターフェース**- ターミナルでのカラフルで直感的な表示\n\n## 🚀 注文可能な商品\n\n| 注文 | 説明 |\n| ----------------------- | ---------------------------- |\n| `python mcp.py collect` | システムデータ収集（CPU、RAM） |\n| `python mcp.py train` | 異常検出のためのAIモデルのトレーニング |\n| `python mcp.py monitor` | 異常検出機能を備えたリアルタイム監視を開始 |\n| `python mcp.py stats` | 使用状況グラフと統計を生成する |\n| `python mcp.py discord` | Discordで詳細なレポートを送信します |\n| `python mcp.py network` | ネットワークを分析し、専用のレポートを送信します |\n| `python mcp.py all` | 完全なシーケンス（収集、トレーニング、監視）を実行します |\n\n## 🛠️ 建築\n\nMCP\\_AI\\_Monitor は、いくつかのアドオンで構成されています。\n\n1. **データ収集モジュール**( `collect_data.py` )\n\n * 定期的にシステムメトリックを記録します\n * 後で分析できるようにデータをCSV形式で保存します\n\n2. **AIトレーニングモジュール**（ `train_model.py` ）\n\n * 収集したデータを前処理する\n * 異常検出のための分離フォレストモデルをトレーニングする\n * リアルタイム使用のためにモデルを保存する\n\n3. **監視モジュール**( `monitor_ai.py` )\n\n * 訓練されたモデルを使用してリアルタイムで異常を検出する\n * 通常の行動に適応するための学習段階を実装します\n * アプリケーションの起動と実際の異常を区別します\n\n4. **Discordとの統合**\n\n * ハードウェアとネットワークの個別のレポートを送信します\n * 各データカテゴリごとに設定可能なWebhookを使用する\n * テーマ別の埋め込みによる最適化されたビジュアルフォーマット\n\n## 📊 Discordレポート\n\nMCP\\_AI\\_Monitor は詳細なレポートを生成し、専用の Webhook 経由で Discord に送信します。\n\n### ハードウェアレポート\n\n* **システム情報**- CPU、RAM、OS に関する詳細\n* **使用状況グラフ**- CPU/RAM の傾向を視覚化\n* **アクティブプロセス**- 最も電力を消費するアプリケーションのリスト\n\n### ネットワークレポート\n\n* **ネットワークアクティビティ**- アップロード/ダウンロード速度、データ量\n* **ネットワークインターフェース**- アクティブなインターフェースとその IP アドレスの詳細\n* **アクティブ接続**- 確立された接続と関連プロセスを追跡します\n\n## 📋 前提条件\n\n* Python 3.8以上\n* Python の依存関係 ( `pip install -r requirements.txt`でインストール可能):\n * psutil - システムデータ収集\n * scikit-learn - 機械学習アルゴリズム\n * pandas - データ操作\n * matplotlib - グラフ生成\n * colorama - ターミナルでのカラフルな表示\n * discord-webhook - Discordとの統合\n\n## 🔧 インストール\n\n1. このリポジトリをクローンします:\n\n```bash\ngit clone https://github.com/MedusaSH/MCP_AI_Monitor.git\ncd MCP_AI_Monitor\n```\n\n2. 依存関係をインストールします。\n\n```bash\npip install -r requirements.txt\n```\n\n3. Discord Webhook を設定します (オプション):\n * `mcp.py`ファイル内のWebhook URLを変更する\n * ハードウェアとネットワークのレポートに別々のWebhookを使用する機能\n\n## 📖 ユーザーガイド\n\n### クイックスタート\n\n初めてフル使用する場合:\n\n```bash\n# Collecte de données (60 secondes par défaut)\npython mcp.py collect\n\n# Entraînement du modèle IA\npython mcp.py train\n\n# Surveillance en temps réel\npython mcp.py monitor\n```\n\n### 自動化されたワークフロー\n\nプロセス全体を 1 つのコマンドで実行するには:\n\n```bash\n# Exécute la séquence complète et envoie un rapport sur Discord\npython mcp.py all --duration 120 --report\n```\n\n## 🔍 異常検出\n\nシステムは、Isolation Forest アルゴリズムを使用して異常な動作を検出します。\n\n1. **学習フェーズ**- ベースラインを確立するためのデータ収集\n2. **動的適応**- 通常の行動に基づいて閾値を調整する\n3. **スマートフィルタリング**- アプリの起動を検出して誤検知を削減\n4. **異常スコアリング**- 異常レベルによるイベントの分類\n\n## 🌱 貢献\n\n貢献を歓迎します!貢献するには:\n\n1. プロジェクトをフォークする\n2. 機能用のブランチを作成します（ `git checkout -b feature/amazing-feature` ）\n3. 変更をコミットします ( `git commit -m 'Add some amazing feature'` )\n4. ブランチにプッシュする ( `git push origin feature/amazing-feature` )\n5. プルリクエストを開く\n\n## 📜 ライセンス\n\nこのプロジェクトは MIT ライセンスに基づいてライセンスされています。詳細については、 `LICENSE`ファイルを参照してください。\n\n## 👥 著者\n\n* **MedusaSH** -*初期開発*- [Github](https://x.com/MedusaActus)\n\n## 🙏 謝辞\n\n* scikit-learnによる森林断熱\n* システムメトリックにアクセスするための psutil\n* Discord 統合用の discord-webhook ライブラリ\n\n***","Japanese","ja-JP",{"_34":65,"_38":69,"_43":70},{"_36":66,"_38":67,"_40":68},"실시간으로 비정상적인 리소스 사용 패턴을 감지하기 위해 비지도 머신 러닝 알고리즘을 사용하는 고급 시스템 모니터링 솔루션으로, 이상 감지, 프로세�� 분석, Discord 통합 등의 기능을 갖추고 있습니다.","MCP AI 모니터","# MCP\\_AI\\_모니터\n\n![MCP AI 모니터](https://img.shields.io/badge/MCP-AI%20Monitor-00c7ff?style=for-the-badge\\&logo=python\\&logoColor=white)![파이썬 3.8 이상](https://img.shields.io/badge/Python-3.8+-blue.svg?style=flat\\&logo=python\\&logoColor=white)![MIT 라이센스](https://img.shields.io/badge/License-MIT-green.svg?style=flat)![버전 1.0.0](https://img.shields.io/badge/Version-1.0.0-orange.svg?style=flat)\n\n## 🔍 개요\n\nMCP\\_AI\\_Monitor는 비지도 학습 알고리즘을 사용하여 리소스 사용에서 비정상적인 동작을 감지하는 포괄적인 시스템 모니터링 솔루션입니다. 실시간으로 시스템 성능에 대한 심층적인 가시성을 제공하도록 설계되었으며, 데이터 수집, 예측 분석 및 상세 보고 기능을 결합합니다.\n\n## ✨ 주요 특징\n\n* **🤖 AI 이상 감지** - Isolation Forest를 사용하여 비정상적인 시스템 동작을 식별합니다.\n* **📊 실시간 분석** - CPU, RAM 및 네트워크 메트릭의 지속적인 모니터링\n* **🧠 적응형 학습** - 시스템의 정상적인 동작에 맞춰 조정하여 거짓 양성을 줄입니다.\n* **📱 즉각적인 알림** - 이상이 감지되면 시스템이 경고합니다.\n* **📈 상세 시각화** - 추세 식별이 가능한 리소스 사용 그래프\n* **⚙️ 프로세스 분석** - 리소스 집약적 애플리케이션 식별\n* **🌐 네트워크 모니터링** - 활성 연결 및 네트워크 성능 분석\n* **📡 Discord 통합** - 자세한 보고서가 Discord 채널로 자동 전송됩니다.\n* **🎨 최신 CLI 인터페이스** - 터미널의 다채롭고 직관적인 디스플레이\n\n## 🚀 주문 가능\n\n| 주문하다 | 설명 |\n| ----------------------- | ---------------------------- |\n| `python mcp.py collect` | 시스템 데이터 수집(CPU, RAM) |\n| `python mcp.py train` | 이상 탐지를 위한 AI 모델 학습 |\n| `python mcp.py monitor` | 이상 감지로 실시간 모니터링 시작 |\n| `python mcp.py stats` | 사용량 그래프와 통계를 생성합니다. |\n| `python mcp.py discord` | Discord에 자세한 보고서를 보냅니다. |\n| `python mcp.py network` | 네트워크를 분석하고 전담 보고서를 보냅니다. |\n| `python mcp.py all` | 전체 시퀀스(수집, 교육, 모니터링)를 수행합니다. |\n\n## 🛠️ 건축\n\nMCP\\_AI\\_Monitor는 여러 개의 추가 기능으로 구성되어 있습니다.\n\n1. **데이터 수집 모듈** ( `collect_data.py` )\n\n * 정기적으로 시스템 메트릭을 기록합니다.\n * 나중에 분석할 수 있도록 CSV 형식으로 데이터를 저장합니다.\n\n2. **AI 학습 모듈** ( `train_model.py` )\n\n * 수집된 데이터를 사전 처리합니다\n * 이상 탐지를 위한 Isolation Forest 모델 학습\n * 실시간 사용을 위해 모델을 저장합니다.\n\n3. **모니터링 모듈** ( `monitor_ai.py` )\n\n * 훈련된 모델을 사용하여 실시간으로 이상을 감지합니다.\n * 정상적인 행동에 적응하기 위한 학습 단계를 구현합니다.\n * 실제 이상 현상과 애플리케이션 실행을 구별합니다.\n\n4. **Discord 통합**\n\n * 하드웨어 및 네트워크에 대한 별도의 보고서를 보냅니다.\n * 각 데이터 범주에 대해 구성 가능한 웹후크를 사용합니다.\n * 주제별 임베드를 통한 최적화된 시각적 형식\n\n## 📊 디스코드 보고서\n\nMCP\\_AI\\_Monitor는 자세한 보고서를 생성하여 전용 웹훅을 통해 Discord로 전송합니다.\n\n### 하드웨어 보고서\n\n* **시스템 정보** - CPU, RAM, OS에 대한 세부 정보\n* **사용량 그래프** - CPU/RAM 추세 시각화\n* **활성 프로세스** - 전력 소모가 가장 많은 애플리케이션 목록\n\n### 네트워크 보고서\n\n* **네트워크 활동** - 업로드/다운로드 속도, 데이터 볼륨\n* **네트워크 인터페이스** - 활성 인터페이스 및 해당 IP 주소의 세부 정보\n* **활성 연결** - 설정된 연결 및 관련 프로세스 추적\n\n## 📋 필수 조건\n\n* 파이썬 3.8 이상\n* Python 종속성( `pip install -r requirements.txt` 를 통해 설치 가능):\n * psutil - 시스템 데이터 수집\n * scikit-learn - 머신 러닝 알고리즘\n * 판다스 - 데이터 조작\n * matplotlib - 그래프 생성\n * colorama - 터미널의 다채로운 디스플레이\n * discord-webhook - Discord와 통합\n\n## 🔧 설치\n\n1. 이 저장소를 복제하세요:\n\n지엑스피1\n\n2. 종속성을 설치합니다.\n\n```bash\npip install -r requirements.txt\n```\n\n3. Discord 웹훅 구성(선택 사항):\n * `mcp.py` 파일에서 웹훅 URL을 변경합니다.\n * 하드웨어 및 네트워크 보고서에 대해 별도의 웹훅을 사용할 수 있는 기능\n\n## 📖 사용자 가이드\n\n### 빠른 시작\n\n처음으로 완전히 사용하려면:\n\n```bash\n# Collecte de données (60 secondes par défaut)\npython mcp.py collect\n\n# Entraînement du modèle IA\npython mcp.py train\n\n# Surveillance en temps réel\npython mcp.py monitor\n```\n\n### 자동화된 워크플로\n\n전체 프로세스를 하나의 명령으로 실행하려면:\n\n```bash\n# Exécute la séquence complète et envoie un rapport sur Discord\npython mcp.py all --duration 120 --report\n```\n\n## 🔍 이상 감지\n\n이 시스템은 Isolation Forest 알고리즘을 사용하여 비정상적인 동작을 감지합니다.\n\n1. **학습 단계** - 기준선을 설정하기 위한 데이터 수집\n2. **동적 적응** - 정상적인 동작에 따라 임계값 조정\n3. **스마트 필터링** - 앱 실행을 감지하여 거짓 양성을 줄입니다.\n4. **이상 점수 매기기** - 비정상 수준에 따른 이벤트 분류\n\n## 🌱 기여\n\n기여를 환영합니다! 기여하려면:\n\n1. 프로젝트를 포크하다\n2. 기능에 대한 브랜치를 만듭니다( `git checkout -b feature/amazing-feature` )\n3. 변경 사항을 커밋하세요( `git commit -m 'Add some amazing feature'` )\n4. 브랜치에 푸시( `git push origin feature/amazing-feature` )\n5. 풀 리퀘스트 열기\n\n## 📜 라이센스\n\n이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여되었습니다. 자세한 내용은 `LICENSE` 파일을 참조하세요.\n\n## 👥 저자\n\n* **MedusaSH** - *초기 개발* - [Github](https://x.com/MedusaActus)\n\n## 🙏 감사의 말\n\n* scikit-learn을 이용한 산림 단열\n* 시스템 메트릭에 액세스하기 위한 psutil\n* Discord 통합을 위한 discord-webhook 라이브러리\n\n***","Korean","ko-KR",{"_34":72,"_38":76,"_43":77},{"_36":73,"_38":74,"_40":75},"一种先进的系统监控解决方案，使用无监督机器学习算法实时检测异常资源使用模式，其功能包括异常检测、流程分析和 Discord 集成。","MCP AI 监控器","# MCP\\_AI\\_监控器\n\n![MCP AI 监控器](https://img.shields.io/badge/MCP-AI%20Monitor-00c7ff?style=for-the-badge\\&logo=python\\&logoColor=white)![Python 3.8+](https://img.shields.io/badge/Python-3.8+-blue.svg?style=flat\\&logo=python\\&logoColor=white)![MIT 许可证](https://img.shields.io/badge/License-MIT-green.svg?style=flat)![版本 1.0.0](https://img.shields.io/badge/Version-1.0.0-orange.svg?style=flat)\n\n## 🔍 概述\n\nMCP\\_AI\\_Monitor 是一个全面的系统监控解决方案，它使用无监督机器学习算法来检测资源使用中的异常行为。它旨在实时深入了解您的系统性能，结合了数据收集、预测分析和详细报告。\n\n## ✨ 主要特点\n\n* **🤖 AI 异常检测**- 使用孤立森林识别异常的系统行为\n* **📊 实时分析**- 持续监控 CPU、RAM 和网络指标\n* **🧠 自适应学习**- 适应系统的正常行为以减少误报\n* **📱 即时通知**- 检测到异常时系统发出警报\n* **📈 详细可视化**- 具有趋势识别的资源使用情况图表\n* **⚙️ 流程分析**- 识别资源密集型应用程序\n* **🌐 网络监控**- 分析活动连接和网络性能\n* **📡 Discord 集成**- 详细报告自动发送到您的 Discord 频道\n* **🎨 现代 CLI 界面**- 终端中色彩丰富且直观的显示\n\n## 🚀 可订购\n\n| 命令 | 描述 |\n| ----------------------- | ----------------- |\n| `python mcp.py collect` | 系统数据收集（CPU、RAM） |\n| `python mcp.py train` | 训练用于异常检测的 AI 模型 |\n| `python mcp.py monitor` | 启动具有异常检测的实时监控 |\n| `python mcp.py stats` | 生成使用情况图表和统计数据 |\n| `python mcp.py discord` | 在 Discord 上发送详细报告 |\n| `python mcp.py network` | 分析网络并发送专门报告 |\n| `python mcp.py all` | 执行完整序列（收集、训练、监控） |\n\n## 🛠️ 建筑\n\nMCP\\_AI\\_Monitor 由几个附加组件组成：\n\n1. **数据收集模块**（ `collect_data.py` ）\n\n * 定期记录系统指标\n * 以 CSV 格式存储数据以供日后分析\n\n2. **AI训练模块**（ `train_model.py` ）\n\n * 预处理收集的数据\n * 训练孤立森林模型进行异常检测\n * 保存模型以供实时使用\n\n3. **监控模块**（ `monitor_ai.py` ）\n\n * 使用训练好的模型实时检测异常\n * 实施学习阶段以适应正常行为\n * 区分应用程序启动与真实异常\n\n4. **Discord 集成**\n\n * 发送硬件和网络的单独报告\n * 为每个数据类别使用可配置的 webhook\n * 通过主题嵌入优化视觉格式\n\n## 📊 Discord 报告\n\nMCP\\_AI\\_Monitor 生成详细报告并通过专用 webhook 将其发送到 Discord：\n\n### 硬件报告\n\n* **系统信息**- 有关 CPU、RAM、OS 的详细信息\n* **使用情况图表**- 可视化 CPU/RAM 趋势\n* **活动进程**- 最耗电的应用程序列表\n\n### 网络报告\n\n* **网络活动**- 上传/下载速度、数据量\n* **网络接口**- 活动接口及其 IP 地址的详细信息\n* **活动连接**- 跟踪已建立的连接和相关进程\n\n## 📋 先决条件\n\n* Python 3.8+\n* Python 依赖项（可通过`pip install -r requirements.txt`安装）：\n * psutil - 系统数据收集\n * scikit-learn——机器学习算法\n * pandas - 数据操作\n * matplotlib - 图形生成\n * colorama - 终端中的彩色显示\n * discord-webhook - 与 Discord 集成\n\n## 🔧 安装\n\n1. 克隆此存储库：\n\n```bash\ngit clone https://github.com/MedusaSH/MCP_AI_Monitor.git\ncd MCP_AI_Monitor\n```\n\n2. 安装依赖项：\n\n```bash\npip install -r requirements.txt\n```\n\n3. 配置您的 Discord webhook（可选）：\n * 更改`mcp.py`文件中的 webhook URL\n * 能够使用单独的 webhook 来报告硬件和网络\n\n## 📖 用户指南\n\n### 快速入门\n\n首次完整使用：\n\n```bash\n# Collecte de données (60 secondes par défaut)\npython mcp.py collect\n\n# Entraînement du modèle IA\npython mcp.py train\n\n# Surveillance en temps réel\npython mcp.py monitor\n```\n\n### 自动化工作流程\n\n要通过一个命令运行整个过程：\n\n```bash\n# Exécute la séquence complète et envoie un rapport sur Discord\npython mcp.py all --duration 120 --report\n```\n\n## 🔍 异常检测\n\n系统采用孤立森林算法检测异常行为：\n\n1. **学习阶段**——收集数据以建立基线\n2. **动态适应**——根据正常行为调整阈值\n3. **智能过滤**——检测应用程序启动以减少误报\n4. **异常评分**- 根据异常级别对事件进行分类\n\n## 🌱 贡献\n\n欢迎投稿！贡献：\n\n1. 分叉项目\n2. 为你的功能创建一个分支（ `git checkout -b feature/amazing-feature` ）\n3. 提交您的更改（ `git commit -m 'Add some amazing feature'` ）\n4. 推送到分支（ `git push origin feature/amazing-feature` ）\n5. 打开拉取请求\n\n## 📜 许可证\n\n该项目已获得 MIT 许可。请参阅`LICENSE`文件以了解更多信息。\n\n## 👥 作者\n\n* **MedusaSH** -*初始开发*- [Github](https://x.com/MedusaActus)\n\n## 🙏 致谢\n\n* 通过 scikit-learn 实现森林隔热\n* psutil 用于访问系统指标\n* 用于 Discord 集成的 discord-webhook 库\n\n***","Chinese","zh-CN","githubUser","locale",{"_81":82,"_20":83,"_90":37,"_91":37,"_23":92,"_93":94,"_38":39,"_124":125,"_132":-5,"_133":-5,"_134":135,"_170":171,"_175":-5,"_101":157,"_176":161,"_8":177,"_178":179},"addedAt","2025-04-19T04:01:59.979916Z",[84,85,86,87,88,89],"category:monitoring","category:observability","environment:local","hosting:local-only","language:python","license_grade:f","descriptionMarkdown","descriptionPlainText",5665,"integrations",[95,105,111,118],{"_96":97,"_36":103,"_8":104},"brand",{"_98":99,"_38":100,"_101":102},"logoSvgPath","M20.317 4.3698a19.7913 19.7913 0 00-4.8851-1.5152.0741.0741 0 00-.0785.0371c-.211.3753-.4447.8648-.6083 1.2495-1.8447-.2762-3.68-.2762-5.4868 0-.1636-.3933-.4058-.8742-.6177-1.2495a.077.077 0 00-.0785-.037 19.7363 19.7363 0 00-4.8852 1.515.0699.0699 0 00-.0321.0277C.5334 9.0458-.319 13.5799.0992 18.0578a.0824.0824 0 00.0312.0561c2.0528 1.5076 4.0413 2.4228 5.9929 3.0294a.0777.0777 0 00.0842-.0276c.4616-.6304.8731-1.2952 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