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Comparative Analysis of Tree-based Intrusion Detection Modelling and Machine Learning Classification Models using Cyber-Security Dataset

by Motlatso Mokoele, Sello Mokwena
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 13
Year of Publication: 2024
Authors: Motlatso Mokoele, Sello Mokwena
10.5120/ijca2024923480

Motlatso Mokoele, Sello Mokwena . Comparative Analysis of Tree-based Intrusion Detection Modelling and Machine Learning Classification Models using Cyber-Security Dataset. International Journal of Computer Applications. 186, 13 ( Mar 2024), 33-40. DOI=10.5120/ijca2024923480

@article{ 10.5120/ijca2024923480,
author = { Motlatso Mokoele, Sello Mokwena },
title = { Comparative Analysis of Tree-based Intrusion Detection Modelling and Machine Learning Classification Models using Cyber-Security Dataset },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2024 },
volume = { 186 },
number = { 13 },
month = { Mar },
year = { 2024 },
issn = { 0975-8887 },
pages = { 33-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number13/comparative-analysis-of-tree-based-intrusion-detection-modelling-and-machine-learning-classification-models-using-cyber-security-dataset/ },
doi = { 10.5120/ijca2024923480 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-03-27T00:44:38.441985+05:30
%A Motlatso Mokoele
%A Sello Mokwena
%T Comparative Analysis of Tree-based Intrusion Detection Modelling and Machine Learning Classification Models using Cyber-Security Dataset
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 13
%P 33-40
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

One of the critical problems organizations encounters is the increasing prevalence of cyber-criminals exploiting vulnerabilities, leading to identity theft. This breach of privacy not only threatens the organization’s financial assets, but can also have long-lasting consequences such as damaged reputations and legal implications. To address these issues, the study presented a thorough comparative analysis between tree-based intrusion detection model and popular machine learning classifiers using the well-established KDD99 dataset. The approach leverages a hybrid feature selection method, integrating the Gini index and information gain within a decision tree framework to enhance model efficiency. Evaluation metrics encompass precision, F1 score, confusion matrix, precision, recall, and execution time. Rigorous dataset preprocessing eliminates noise and biases. The findings reveal nuanced insights into model strengths and weaknesses, emphasizing the efficacy of the hybrid feature selection method in tree-based models. This study offers valuable guidance for cybersecurity professionals, helping to select models based on specific performance criteria. Ultimately, the research contributes to the advancement of intrusion detection techniques, highlighting potential areas for further exploration and improvement in the pursuit of more efficient and accurate intrusion detection systems.

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Index Terms

Computer Science
Information Sciences
Cyber Threats
Data Preprocessing
Evaluation Metrics
Classification Models
Digital Landscape
Denial-of-Service
Internet of Things

Keywords

Cybersecurity intrusion detection machine learning hybrid feature selection tree-based intrusion detection modeling Gini index Information Gain

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