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

Dublin Core

Title

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

Subject

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

Description

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

Creator

Yuri Prihantono1
, Kalamullah Ramli2

Publisher

Universitas Indonesia

Date

29-04-2022

Contributor

Fajar bagus W

Format

PDF

Language

Indnesia

Type

Text

Files

Collection

Citation

Yuri Prihantono1 , Kalamullah Ramli2, “Model-Based Feature Selection for Developing Network Attack Detection
and Alerting System,” Repository Horizon University Indonesia, accessed June 6, 2025, https://repository.horizon.ac.id/items/show/9164.