Network Root Cause Analysis Tool

Overview

This Network Root Cause Analysis Tool is a comprehensive suite designed for network administrators and IT professionals to efficiently monitor, analyse, and troubleshoot network issues. The application combines advanced network monitoring capabilities with machine learning-based anomaly detection to provide a complete network management solution.

What Problem Does It Solve?

Network administrators face numerous challenges in maintaining network health and security:

• Difficulty in identifying the root cause of network issues quickly
• Lack of real-time visibility into network traffic and device behaviour
• Challenges in detecting and responding to network anomalies
• Time-consuming manual network scanning and reporting processes

Key Benefits

1. Unified Monitoring Interface

   • Single dashboard for all network monitoring needs
   • Real-time visibility into network performance
   • Intuitive visualisation of network metrics

2. Automated Issue Detection

   • Machine learning-based anomaly detection
   • Instant SMS alerts for critical issues
   • Proactive identification of potential problems

3. Enhanced Security

   • Continuous network vulnerability scanning
   • Device fingerprinting and tracking
   • Unauthorized device detection

4. Time-Saving Features

   • Automated report generation
   • Quick troubleshooting workflows
   • Batch scanning capabilities

Technical Innovation

The tool leverages several cutting-edge technologies:

• Machine Learning for anomaly detection
• Real-time packet analysis using advanced filtering
• Automated vulnerability assessment
• WiFi signal strength analysis and optimisation
• Integration with Twilio for instant notifications

Features

• Central Dashboard: Unified control centre for accessing all modules
• Real-time Packet Analysis: Capture and analyse network packets with filtering capabilities
• Network Device Scanner: Discover devices on your network and scan for vulnerabilities
• WiFi Signal Analyzer: Monitor and analyze wireless networks and signal strength
• Anomaly Detection: Machine learning-based system to identify unusual network behavior
• SMS Alert System: Receive notifications when network anomalies are detected
• Reporting System: Generate comprehensive PDF reports of network status and issues
• Network Troubleshooting: Tools to diagnose and resolve common network problems

Technical Architecture

Core Components

1. Network Analysis Engine

   • Built with Python and Scapy for packet capture and analysis
   • Real-time traffic monitoring and filtering
   • Deep packet inspection capabilities

2. Machine Learning Module

   • Anomaly detection using advanced ML algorithms
   • Pattern recognition for network behavior analysis
   • Continuous learning from network traffic patterns

3. Security Scanner

   • Port scanning and vulnerability assessment
   • Device fingerprinting and OS detection
   • Network topology mapping

4. Alert System

   • SMS notifications via Twilio integration
   • Configurable alert thresholds
   • Alert history tracking and analysis

5. Reporting Engine

   • PDF report generation with detailed metrics
   • Historical data analysis
   • Customizable report templates

Technology Stack

• Frontend: PyQt6 for the graphical user interface
• Backend: Python 3.9+ for core functionality
• Libraries:
  • Scapy for packet manipulation
  • Pandas for data analysis
  • Matplotlib for data visualization
  • NumPy for numerical computations
  • Twilio for SMS notifications

System Requirements

• Windows 10/11 (64-bit)
• Python 3.9 or higher
• Administrator privileges (required for packet capture)
• Minimum 4GB RAM
• 1GB free disk space

Installation

Step 1: Clone or Download the Repository

git clone https://github.com/yourusername/network-root-cause-analysis.git
cd network-root-cause-analysis

Step 2: Create a Virtual Environment (Recommended)

python -m venv venv
venv\Scripts\activate

Step 3: Install Required Dependencies

pip install -r requirements.txt

The requirements.txt file includes all necessary libraries like PyQt6, matplotlib, numpy, scapy, pandas, and more.

Step 4: Optional - Install Twilio for SMS Alerts

If you want to use real SMS alerts (instead of simulated ones):

pip install twilio

Configuration

SMS Alert Configuration

1. Open sms_alert_settings.json in the root directory
2. Set "enabled" to true to enable SMS alerts
3. Update "phone_number" with your phone number in international format (e.g., "+1234567890")

Example configuration:

{
    "enabled": true,
    "phone_number": "+1234567890",
    "alert_history": []
}

Twilio Configuration (Optional)

If you want to use real SMS alerts with Twilio:

1. Sign up for a Twilio account at https://www.twilio.com
2. Get your Account SID and Auth Token from the Twilio dashboard
3. Create a file named secret.py in the SMS gateway directory with:

account_sid = "your_account_sid"
auth_token = "your_auth_token"
twilio_number = "your_twilio_phone_number"

Usage

Running the Application

1. Ensure you're in the project directory
2. Run the main application with administrator privileges:

python Main.py

Module Descriptions

• Main Dashboard: Central hub for accessing all tools
• Network Monitoring: Real-time tracking of network performance metrics
• Network Troubleshooting: Tools for diagnosing common network issues
• Network Device Scanner: Discovers devices and checks for vulnerabilities
• WiFi Scanner: Analyzes wireless networks and signal strength
• Real-time Packet Filtering: Captures and analyzes network packets
• Report Generator: Creates PDF reports of network status and issues

Data Storage

The application stores temporary data in the data_temp directory, including:

• Packet analysis data
• WiFi scanning results
• Device scanning results
• Network monitoring data
• Troubleshooting history

Troubleshooting

Common Issues

1. Permission Errors: Run the application as Administrator
2. Missing Modules: Verify all dependencies are installed
3. Packet Capture Issues: Install WinPcap or Npcap on Windows
4. SMS Alerts Not Working: Check your SMS configuration

Logs

Check the console output for error messages and debugging information.

License

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

Project Documentation

The detailed project documentation is available in the following formats:

• Final Report - Comprehensive project documentation and analysis
• Network Reports - Sample network analysis reports generated by the tool

Acknowledgments

This project was developed as a university final year project, utilizing Python, PyQt6, and various open-source libraries.