ML Model for Neptune (SYN flood) attacks in network activity.

This project develops a binomial classification ML model to detect Neptune (SYN flood) attacks in network activity. Using 86,845 labeled training records, the model distinguishes between normal and attack activities. Preprocessing includes handling missing values, encoding categorical variables, and feature scaling. A Random Forest Classifier, tuned via Grid Search for hyperparameter optimization, is evaluated using F1 score and accuracy. The final model predicts attacks on a test set of 21,712 records, with results prepared for submission.