Machine Learning-Based Classification Approach for Network Intrusion Detection System

Document Type

Conference Proceeding

Source of Publication

2024 15th Annual Undergraduate Research Conference on Applied Computing (URC)

Publication Date

4-25-2024

Abstract

Today's digital world is constantly under threat from hackers and harmful programs, making it essential to have strong systems in place to detect and stop these attacks. This paper contributes to the ongoing effort to enhance network intrusion detection system (NIDS) by exploring the application of various supervised machine learning models. The goal is to identify the best-performing machine learning model for NIDS. Specifically, the study examines the efficacy of Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), and XGBoost models in detecting network intrusions. The study utilized 47,736 records from a publicly accessible source. RF and XGBoost have the highest F1 scores and accuracy rates among the models tested, with both achieving an F1 score of 99.4% and an accuracy rate of 99.3%, while LR had the lowest performance among the models deployed.

ISBN

979-8-3315-2734-1

Publisher

IEEE

Volume

00

First Page

1

Last Page

6

Disciplines

Computer Sciences

Keywords

Network intrusion detection system, Machine learning, Supervised learning, Random forest, XGBoost

Indexed in Scopus

no

Open Access

no

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