Cybersecurity / Machine Learning

Network Intrusion
Detection

ML model using XGBoost to predict and classify network intrusion attacks in real network traffic data with high precision across multiple attack categories.

Python XGBoost Scikit-learn Pandas NumPy Google Colab
Network Intrusion Detection
The Problem

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

The Solution

An end-to-end ML pipeline covering preprocessing, class imbalance correction with SMOTE, XGBoost model training with cross-validated hyperparameter tuning, and rigorous evaluation. Feature importance scores reveal which network traffic attributes are most predictive of attack behaviour.

Key Features
—Multi-class classification: DoS, Probe, R2L, U2R, Normal
—Feature engineering and selection from raw network logs
—XGBoost model with cross-validated hyperparameter tuning
—SMOTE oversampling to handle severe class imbalance
—Evaluation: F1-score, precision, recall, confusion matrix
—Feature importance analysis to identify key traffic indicators
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