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
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
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
, 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.
and Alerting System,” Repository Horizon University Indonesia, accessed June 6, 2025, https://repository.horizon.ac.id/items/show/9164.