Neptune Guard: Network Intrusion Detection System

Neptune Guard is a machine learning project designed to detect and classify Neptune (SYN flood) attacks in network traffic. Utilizing a comprehensive dataset of network connection attributes, this system employs advanced classification algorithms to distinguish between normal network activity and potential Neptune attacks. The project encompasses data preprocessing, feature engineering, model training, and evaluation, culminating in a robust prediction system for identifying malicious network behavior. Key features of Neptune Guard include: *Processing and analysis of extensive network traffic data *Handling of both categorical and numerical features *Implementation of Random Forest and Gradient Boosting classifiers *Scalable pipeline for data preprocessing and model training *Performance evaluation using standard classification metrics *Generation of predictions for unseen network traffic data This project serves as a valuable tool for network security professionals and researchers, offering insights into effective strategies for detecting and mitigating Neptune attacks in real-world scenarios.