Anomaly Detection In Computer Networks Using Machine Learning Techniques

This research aims at identifying the anomalies in computer network traffic by employing machine learning algorithm. It assesses basic models such as Random Forest, Support Vector Machine (SVM), k-Nearest Neighbors (kNN), etc., as well as complex algorithms like XGBoost, LightGBM, and CatBoost, etc. Thus, through real-life scenarios, overcoming complications like inadequacy of classes addressed in the SMOTE approach, the study shows enhancements in the area of detection accuracy and effectiveness. The contribution of this work is to improve the security by developing a large and effective scheme for detecting abnormally in the traffic of the network.