System Process Monitor with Anomaly Detection

📜 Overview

This project is a System Process Monitor that utilizes advanced machine learning techniques to detect anomalies in real-time. Designed for cross-platform compatibility, it provides detailed process monitoring, anomaly detection, and customizable reporting features, making it suitable for cybersecurity and systems management applications.

🛠️ Features

• Real-Time Process Monitoring:
  • Collects and displays metrics such as CPU usage, memory usage, thread count, and more.
• Anomaly Detection:
  • Employs the Isolation Forest algorithm to identify suspicious processes based on behavior patterns.
• Customizable Reports:
  • Automatically generates detailed JSON reports for detected anomalies.
• Interactive UI:
  • A user-friendly interface built with PyQt5 for visualizing system metrics and anomalies.

🔧 Technical Details

• Languages: Python, C++
• Libraries Used:
  • psutil: For system process and resource monitoring.
  • sklearn: For implementing the Isolation Forest anomaly detection algorithm.
  • PyQt5: For building an interactive desktop application.
• Platforms: Supports macOS (for now)

🚀 How to run the project?

Pre-requisites

• Python 3.7+
• Required Python libraries: psutil, scikit-learn, PyQt5, numpy Install dependencies using : pip install -r requirements.txt

Steps to run the project

1. Clone the repository

   git clone https://github.com/umadhatri/System-Monitor.git
   cd system-process-monitor

2. Start the UI application:

   python process_monitor_ui.py

3. Monitor processes and detect anomalies in real time!

🎥 Demo

Check out the video demonstration here showcasing the real-time anomaly detection capabilities of the program!

📊 JSON Output of the system

Please check the JSON file in the repository to see how the detected anomalies are stored in a JSON file

🔒 Applications

• Cybersecurity: Detect malware-like behavior (e.g., excessive resource usage or unusual network activity)
• System Administration: Monitor system performance and identify rogue processes
• Research: Analyze real-world process behavior patterns for anomaly prediction

🤝🏻 Contributions

Contributions are welcome! Feel free to open issues or submit pull requests to enhance the project.

📝 License

This project is licensed under the MIT License. See the LICENSE file for details.

📬 Contact

Please feel free to contact me at umasatyanarayana35@gmail.com for more queries and questions