Network Intrusion Detection Using Machine Learning
Network Intrusion Detection Using Machine Learning
In this video we have performed a comprehensive analysis, from data preprocessing and feature selection to model training, evaluation, and performance comparison. Also includes hyperparameter optimization and cross-validation to ensure robust model evaluation.
Links -
Dataset - https://www.kaggle.com/datasets/sampadab17...
Model (Google Colab) - https://colab.research.google.com/drive/1V...
Fundamental Details -
Data Preprocessing - handling missing values, encoding categorical variables, and scaling features.
Feature Selection - reduce dimensionality and enhance model performance by selecting the most relevant features , Recursive Feature Elimination (RFE) is used in conjunction with Random Forest, which is a powerful ensemble learning method.
Model Training and Hyperparameter Tuning - Logistic Regression: A linear model for binary classification, KNN: A non-parametric method that classifies based on the closest training examples, Decision Tree: A model that splits data into subsets based on feature values to make predictions.
Optuna - hyperparameter optimization framework that efficiently searches for optimal parameters.
Cross-Validation - evaluate model performance by splitting the data into multiple folds and training/testing the model on these folds. prevent overfitting and provides a more reliable estimate of model performance.
Evaluation Metrics - Confusion Matrix gives insights into the true positives, false positives, true negatives, and false negatives.
Classification Report: gives precision, recall, F1-score, and support for each class.
F1-Score - Harmonic mean of precision and recall, which used as a metric to evaluate model performance, especially in imbalanced datasets.
Goal -
Comparing the models performances based on training and test scores.
Plotting Cross-validation scores (precision and recall) are for each model.
Get detailed evaluation of Confusion matrices and classification reports.
Plotting F1-scores to visualize and compare the effectiveness of the models.
Thanks For Watching
#machinelearning #networksecurity #optuna