The document outlines the creation and analysis of a dataset named Hikari-2021, focusing on generating network data for cybersecurity purposes. After describing the methodology for dataset generation and various traffic profiles (background traffic, benign profile, and attacker profile), it delves into data preparation processes, including data understanding, cleaning, outlier detection, and feature selection. The issue of learning from imbalanced data is also addressed. The classification phase involves various techniques, including cross-validation and hyperparameter tuning for boosting algorithms such as Gradient Boosting, XGBoost, and bagging techniques like Random Forest. Finally, the last chapter discusses evaluation metrics and classifier performance.