Network Intrusion
Detection

Overview

This project builds a machine learning model to detect and classify network intrusion attacks from network traffic features. Using the XGBoost gradient boosting framework, the model distinguishes between normal traffic and various attack categories with high accuracy.

Problem

Network intrusions are a major cybersecurity threat. Traditional signature-based detection systems miss novel attacks. ML-based approaches can learn patterns from historical data and generalize to new, unseen attack variants — making them a valuable complement to rule-based systems.

Key Features

• Multi-class classification of network attacks (DoS, Probe, R2L, U2R, Normal)
• Feature engineering and selection from raw network traffic logs
• XGBoost model with hyperparameter tuning via cross-validation
• SMOTE-based oversampling to handle class imbalance
• Detailed evaluation: F1-score, precision, recall, confusion matrix
• Feature importance analysis to identify key traffic indicators

Methodology

The pipeline covers data preprocessing (handling missing values, encoding categorical features, normalization), class imbalance correction, model training with XGBoost, and rigorous evaluation on held-out test data. Feature importance scores reveal which network traffic attributes are most predictive of attack behavior.

Outcome

The model achieves strong F1 scores across attack categories, demonstrating that gradient boosting is highly effective for tabular network intrusion data. The project reinforces understanding of both cybersecurity concepts and applied machine learning workflows.