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

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

Machine Learning, Feature Selection, IDS, Snort, ELK Stack

The use of the Intrusion Detection Systems (IDS) still has 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 in order 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

Yuri Prihantono1
, Kalamullah Ramli2

Universitas Indonesia

29-04-2022

Fajar bagus W

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Indnesia

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