Model-Based Feature Selection for Developing Network Attack Detection and Alerting System

Abstract

Intrusion Detection Systems (IDS) still have unresolved problems, namely the lack of accuracy in attack detection, resulting in false-positive problems and many false alarms. Machine learning is one way that is often utilized to overcome challenges that arise during the implementation of IDS. We present a system that uses a machine learning approach to detect network attacks and send attack alerts in this study. The CSE-CICIDS2018 Dataset and Model-Based Feature Selection technique are used to assess the performance of eight classifier algorithms in identifying network attacks to determine the best algorithm. The resulting XGBoost Model is chosen as the model that provides the highest performance results in this comparison of machine learning models, with an accuracy rate of 99 percent for two-class classification and 98.4 percent for multi-class classification.

Keywords

• machine learning
• feature selection
• ids
• snort
• elk stack