Introduction to EyeNet
Welcome to EyeNet, an advanced network monitoring and management system that combines traditional networking capabilities with cutting-edge machine learning for intelligent network operations.
What is EyeNet?
EyeNet is a comprehensive network management solution that provides:
- Real-time Network Monitoring: Continuous monitoring of network devices, traffic patterns, and performance metrics
- Machine Learning Integration: Intelligent anomaly detection and predictive analytics
- Multi-vendor Support: Compatible with various network devices including pfSense, MikroTik, and OpenDaylight controllers
- Automated Response: Intelligent automation for common network issues and security threats
- Advanced Analytics: Deep insights into network behavior and performance trends
Key Features
1. Network Management
- Device discovery and inventory management
- Configuration management and backup
- Performance monitoring and optimization
- Network topology visualization
2. Machine Learning Capabilities
- Traffic pattern analysis
- Anomaly detection
- Bandwidth prediction
- Security threat detection
3. Security Features
- Real-time threat detection
- Automated security responses
- Access control management
- Security policy enforcement
4. Analytics and Reporting
- Customizable dashboards
- Performance reports
- Capacity planning insights
- Compliance reporting
Getting Started
- Check out our Installation Guide to set up EyeNet
- Read the Architecture Overview to understand the system
- Follow the Development Guide to start contributing
- Explore the API Reference for integration details
System Requirements
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Hardware Requirements:
- CPU: 4+ cores
- RAM: 8GB minimum (16GB recommended)
- Storage: 50GB minimum
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Software Requirements:
- Node.js v18 or higher
- MongoDB v6 or higher
- Modern web browser
- Docker (optional)
Support and Community
- GitHub Issues for bug reports and feature requests
- Technical documentation and guides
- Community forums and discussions
- Professional support options
Next Steps
Ready to dive in? Start with: