Network Intrusion Detection: Model Training and Evaluation

The network intrusion detection code typically involves loading and preprocessing the dataset (e.g., NSL-KDD), encoding categorical features, and normalizing numerical ones. To address class imbalance, techniques like oversampling (e.g., SMOTE) or undersampling are applied. After splitting the data into training and testing sets, classification models like Random Forest, Gradient Boosting, or Logistic Regression are trained. Feature selection (e.g., PCA) may be used to reduce dimensionality. The model’s performance is evaluated using precision, recall, F1-score, and a confusion matrix. Once trained, the model can be saved for real-time deployment in detecting network intrusions.