A lightweight container-based network emulation system.
Network topology map using Netbox as a data source
A vendor-agnostic NMS for carrier-grade network simulation and automation
Network Automations Fabric
An end-to-end Python-based Infrastructure as Code framework for network automation and orchestration.
This script is designed to analyze network traffic captured in a .pcap file using the pyshark library. // Este script está diseñado para analizar el tráfico de red capturado en un archivo .pcap utilizando la librería pyshark.
A structured and beginner-friendly guide to the fundamentals of computer networking covering everything from IP addressing, subnetting, routing, and protocols to network security and troubleshooting.
Professional network engineering toolkit with 16+ tools - subnet calculator, VLSM planner, VLAN manager, routing tools, and more. Web + Electron desktop app.
This is my repo following the YouTube course "Free CCNA 200-301 Complete Course" by Jeremy's IT Lab.
📟 Cable Modem firmware 💿 upgrade solution using PHP 🚀, *single file*
Hands-on CCNA lab portfolio built in GNS3, VMware, and Cisco Packet Tracer | VLANs, routing, ACLs, NAT/PAT, troubleshooting, and network design
Script Go que captura y analiza el tráfico de red y paquetes en tiempo real que pasan a través de interfaces de red. // Go script that captures and analyzes real-time network traffic passing through network interfaces.
Networking, Programming & Automation Showcases
🔄 Automate Cisco switch config retrieval and create formatted documentation with this dual-script setup for efficient IT management.
AlpiNet is a lightweight, Alpine-based Docker image designed specifically for use as a virtual PC in GNS3. It comes pre-installed with a comprehensive suite of networking utilities and tools, making it perfect for network testing, troubleshooting, and education.
Comprehensive list of BGP, ASN, routing, peering, and internet infrastructure analysis tools for defenders, OSINT practitioners, and network engineers.
This project detects structural network anomalies using a GNN autoencoder. It contrasts this deep learning approach with the classic DBSCAN method. While DBSCAN only uses node features (CPU, RAM), the GNN learns the graph's topology to identify statistically improbable links, proving superior for structural analysis.
Powered up shell for Network Engineers
A native Qt GUI application to calculate the theoretical maximum throughput for 4G (LTE) and 5G (NR) mobile networks
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