C# SIEM Core - Next-Generation Security Information and Event Management

A comprehensive SIEM (Security Information and Event Management) system built with C# and .NET 8, designed to address the deficiencies of existing SIEM solutions in the market.

🚀 Features

• Intelligent Threat Detection: Multi-layered detection combining machine learning, behavioral analytics, and threat intelligence
• Real-time Event Processing: High-performance event ingestion and correlation engine
• Advanced Analytics: Built-in UEBA (User and Entity Behavior Analytics) and anomaly detection
• Integrated SOAR: Native security orchestration, automation, and response capabilities
• Modern Web UI: Intuitive, responsive interface with natural language query support
• Open Architecture: Extensible plugin system with comprehensive REST APIs
• Cloud-Native: Microservices-based architecture with horizontal scaling
• Comprehensive Alerting: Multi-channel notifications with intelligent deduplication

🏗️ Architecture

The system follows a microservices architecture with the following components:

• Data Collection Layer: Ingests logs from various sources (Syslog, Windows Events, APIs)
• Event Processing Engine: Normalizes, enriches, and correlates security events
• Analytics Layer: Machine learning models for threat detection and behavioral analysis
• Alert Management: Intelligent alert generation and lifecycle management
• User Interface: Modern web-based dashboard and investigation tools

🛠️ Technology Stack

• Backend: ASP.NET Core 8.0, C#
• Storage: In-memory stores (production would use Elasticsearch/SQL Server)
• Machine Learning: ML.NET integration
• API: RESTful APIs with Swagger documentation
• Authentication: JWT-based authentication (ready for integration)

📋 Prerequisites

• .NET 8.0 SDK
• Visual Studio 2022 or VS Code (optional)
• Git

🚀 Quick Start

1. Clone the repository

   git clone <repository-url>
   cd CSharpSIEM/SiemCore

2. Build the project

   dotnet build

3. Run the application

   dotnet run --urls="http://localhost:5000"

4. Access the API

   • Swagger UI: http://localhost:5000
   • Health Check: http://localhost:5000/health

📖 API Documentation

Event Ingestion

POST /api/siem/events
Content-Type: application/json

{
  "timestamp": "2025-10-10T12:00:00Z",
  "sourceSystem": "Windows Server",
  "eventType": "Authentication",
  "severity": "Medium",
  "description": "User login attempt",
  "sourceIp": "192.168.1.100",
  "username": "john.doe",
  "rawLog": "Event log data..."
}

Query Events

GET /api/siem/events?startTime=2025-10-10T00:00:00Z&severity=High&page=1&pageSize=50

Get Alerts

GET /api/siem/alerts?status=Open&severity=Critical

Update Alert Status

PUT /api/siem/alerts/{alertId}/status
Content-Type: application/json

{
  "status": "Resolved",
  "resolution": "False positive - legitimate admin activity"
}

🔧 Configuration

The system includes several configurable services:

Data Sources

Configure various log sources through the DataSource model:

• Syslog servers
• Windows Event Logs
• Database audit logs
• Custom APIs

Correlation Rules

Create custom threat detection rules:

• Time-based correlation
• Pattern matching
• Machine learning models
• Threshold-based alerting

Notification Channels

Configure alert notifications:

• Email notifications
• Slack integration
• SMS alerts
• Webhook endpoints

🧪 Testing

Sample Event Ingestion

curl -X POST "http://localhost:5000/api/siem/events" \
  -H "Content-Type: application/json" \
  -d '{
    "timestamp": "2025-10-10T12:00:00Z",
    "sourceSystem": "Test System",
    "eventType": "Security",
    "severity": "High",
    "description": "Suspicious activity detected",
    "sourceIp": "192.168.1.100",
    "username": "testuser"
  }'

Health Check

curl http://localhost:5000/health

📊 Dashboard Statistics

Access real-time SIEM statistics:

GET /api/siem/dashboard/stats

Returns:

• Total events processed
• Recent event counts
• Severity distribution
• Top event sources
• System health metrics

🔒 Security Features

• Threat Intelligence Integration: Real-time threat indicator checking
• Behavioral Analytics: User and entity behavior monitoring
• Anomaly Detection: Machine learning-based anomaly scoring
• False Positive Reduction: Adaptive learning from analyst feedback
• Automated Response: Configurable response playbooks

🚀 Production Deployment

For production deployment, consider:

1. Database Integration: Replace in-memory stores with Elasticsearch and SQL Server
2. Message Queue: Implement RabbitMQ or Apache Kafka for event streaming
3. Load Balancing: Deploy multiple API instances behind a load balancer
4. Monitoring: Integrate with Application Insights or similar monitoring solutions
5. Security: Implement proper authentication and authorization
6. Scaling: Deploy on Kubernetes for automatic scaling

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Submit a pull request

📄 License

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

🆘 Support

For support and questions:

• Create an issue in the repository
• Check the API documentation at /swagger
• Review the comprehensive analysis document

🔮 Roadmap

• Web-based dashboard UI
• Advanced machine learning models
• Threat hunting capabilities
• Multi-tenancy support
• Enterprise SSO integration
• Advanced reporting engine
• Mobile application
• Cloud deployment templates

---

Note: This is a demonstration implementation showcasing modern SIEM architecture and capabilities. For production use, additional security hardening, performance optimization, and enterprise features would be required.