The Virtual Machine Introspection (VMI) project is a Python-based tool that utilizes interactive reports to provide insights into virtual machine activity. It is designed for users who require in-depth monitoring and analysis of virtual machine behavior, such as system administrators and security professionals. The project's unique aspect lies in its ability to collect and integrate data from various monitoring functions, including memory, processes, syscalls, files, and network activity, to provide a comprehensive view of the virtual machine's behavior.
The VMI project leverages a range of dependencies, including libvmi, numpy, pandas, scikit-learn, and tensorflow, to analyze the collected data and detect anomalies. The project's primary goal is to provide a robust and scalable solution for virtual machine introspection, enabling users to identify potential security threats and optimize system performance.
The project's architecture is designed to be modular and extensible, allowing users to easily integrate new monitoring functions and analysis modules. The project's core components include data collection, data analysis, and reporting, which work together to provide a seamless and intuitive user experience.
- 🔥 Memory Monitoring — Extracts advanced features from memory data, including statistical moments and entropy.
- 📊 Process Monitoring — Extracts features from process data, including statistical measures and distributions.
- 📈 Syscall Monitoring — Extracts features from syscall data, including frequency, statistical distributions, and time series analysis.
- 📁 File Monitoring — Monitors file activity, including file access and modification.
- 📡 Network Monitoring — Monitors network activity, including packet capture and analysis.
- 🚨 Anomaly Detection — Uses machine learning algorithms to detect anomalies in virtual machine behavior.
- 📊 Data Analysis — Provides advanced data analysis capabilities, including data visualization and statistical analysis.
Python 3.8 or later, libvmi, numpy, pandas, scikit-learn, tensorflow, and other dependencies listed in the requirements.txt file.
git clone https://github.com/likith-sg/VMI.git
cd VMI
pip install -r requirements.txtpython main.pyThe VMI project can be used to monitor and analyze virtual machine activity in a variety of scenarios, including:
- Monitoring system performance and identifying bottlenecks
- Detecting potential security threats, such as malware or unauthorized access
- Analyzing system behavior and optimizing system configuration
- Providing insights into virtual machine activity for auditing and compliance purposes
VMI/
main.py # Main entry point for the application
analysis_modules.py # Data analysis and feature extraction modules
gui_module.py # Graphical user interface module
reporting_module.py # Reporting and visualization module
vmi_integration.py # VMI integration module
requirements.txt # List of dependencies required by the project
| Technology | Version | Purpose |
|---|---|---|
| Python | 3.8+ | Programming language |
| libvmi | latest | Virtual machine introspection library |
| numpy | latest | Numerical computing library |
| pandas | latest | Data analysis library |
| scikit-learn | latest | Machine learning library |
| tensorflow | latest | Machine learning library |
| matplotlib | latest | Data visualization library |
| plotly | latest | Data visualization library |
| tkinterdnd2 | latest | Graphical user interface library |
| Pillow | latest | Image processing library |
The project uses a configuration file (config.yaml) to store settings and parameters. The configuration file can be modified to customize the project's behavior and settings.
- Fork the repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
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