Real Time Network-Based Intrusion Detection System

This research explores a real-time, custom-built Network Intrusion Detection System (NIDS) capable of live packet capture, visualization, and threat detection based on known malicious patterns such as command injection, DNS tunnelling, and reverse shells. It includes practical simulation tools for demonstrable cybersecurity education. This system integrates live packet sniffing with an intelligent pattern recognition engine and uses behaviour-based indicators to detect potential intrusions in Wi-Fi-based environments. Through testing with real and simulated traffic, the tool provides a reliable, demonstrable NIDS model for academic and small enterprise use. The system is lightweight, requires minimal setup, and operates in cross-platform environments including Windows and Linux. It supports modular extensions for future research, including integration with machine learning models and remote alerting systems.