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<!DOCTYPE html> <html xmlns="http://www.w3.org/1999/xhtml"> <head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"> <title>Intrusion Detection System(IDS) Development Using Tree-Based Machine Learning Algorithms</title>  <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta http-equiv="X-UA-Compatible" content="ie=edge"> <meta name="title" content="Intrusion Detection System(IDS) Development Using Tree-Based Machine Learning Algorithms"> <meta name="description" content="The paper proposes a two-phase classification method for detecting anomalies in network traffic, aiming to tackle the challenges of imbalance and feature selection. The study uses Information Gain to select relevant features and evaluates its performance on the CICIDS-2018 dataset with various classifiers. Results indicate that the ensemble classifier achieved the highest accuracy, precision, and recall. The proposed method addresses challenges in intrusion detection and highlights the effectiveness of ensemble classifiers in improving anomaly detection accuracy. Also, the quantity of pertinent characteristics chosen by Information Gain has a considerable impact on the F1-score and detection accuracy. Specifically, the Ensemble Learning achieved the highest accuracy of 98.36% and F1-score of 97.98% using the relevant selected features."/> <meta name="keywords" content="Intrusion Detection System, Anomaly Detection,Imbalance Data, Feature Selection, CICIDS-2018 dataset"/>  <meta name="dc.title" content="Intrusion Detection System(IDS) Development Using Tree-Based Machine Learning Algorithms "> <meta name="citation_authors" content="Witcha Chimphlee"> <meta name="citation_authors" content="Siriporn Chimphlee"> <meta name="dc.type" content="Article"> <meta name="dc.source" content="International Journal of Computer Networks & Communications (IJCNC) Vol.15, No.4"> <meta name="dc.date" content="2023/07/30"> <meta name="dc.identifier" content="10.5121/ijcnc.2023.15406"> <meta name="dc.publisher" content="AIRCC Publishing Corporation"> <meta name="dc.rights" content="http://creativecommons.org/licenses/by/3.0/"> <meta name="dc.format" content="application/pdf"> <meta name="dc.language" content="en"> <meta name="dc.description" content=" The paper proposes a two-phase classification method for detecting anomalies in network traffic, aiming to tackle the challenges of imbalance and feature selection. The study uses Information Gain to select relevant features and evaluates its performance on the CICIDS-2018 dataset with various classifiers. Results indicate that the ensemble classifier achieved the highest accuracy, precision, and recall. The proposed method addresses challenges in intrusion detection and highlights the effectiveness of ensemble classifiers in improving anomaly detection accuracy. Also, the quantity of pertinent characteristics chosen by Information Gain has a considerable impact on the F1-score and detection accuracy. Specifically, the Ensemble Learning achieved the highest accuracy of 98.36% and F1-score of 97.98% using the relevant selected features."/> <meta name="dc.subject" content="Intrusion Detection System"> <meta name="dc.subject" content="Anomaly Detection"> <meta name="dc.subject" content="Imbalance Data"> <meta name="dc.subject" content="Feature Selection"> <meta name="dc.subject" content=" CICIDS-2018 dataset"> <meta name="dc.subject" content="communication"> <meta name="dc.subject" content="information"> <meta name="dc.subject" content="Proceedings"> <meta name="dc.subject" content="Computer Science"> <meta name="dc.subject" content="Technology"> <meta name="dc.subject" content="open access proceedings">   <meta name="prism.publicationName" content="International Journal of Computer Networks & Communications (IJCNC) "> <meta name="prism.publicationDate" content="2023/07/30"> <meta name="prism.volume" content="15"> <meta name="prism.number" content="04"> <meta name="prism.section" content="Article"> <meta name="prism.startingPage" content="93">   <meta name="citation_journal_title" content="International Journal of Computer Networks & Communications (IJCNC)"> <meta name="citation_publisher" content="AIRCC Publishing Corporation"> <meta name="citation_authors" content="Witcha Chimphlee and Siriporn Chimphlee"> <meta name="citation_title" content="Intrusion Detection System(IDS) Development Using Tree-Based Machine Learning Algorithms"> <meta name="citation_online_date" content="2023/07/30"> <meta name="citation_issue" content="15"> <meta name="citation_firstpage" content="93"> <meta name="citation_authors" content="Witcha Chimphlee"> <meta name="citation_authors" content="Siriporn Chimphlee"> <meta name="citation_doi" content="10.5121/ijcnc.2023.15406"> <meta name="citation_abstract_html_url" content="https://aircconline.com/abstract/ijcnc/v15n4/15423cnc06.html"> <meta name="citation_pdf_url" content="https://aircconline.com/ijcnc/V15N4/15423cnc06.pdf">   <meta property="og:site_name" content="AIRCC" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://aircconline.com/abstract/ijcnc/v15n4/15423cnc06.html"> <meta property="og:title" content="Intrusion Detection System(IDS) Development Using Tree-Based Machine Learning Algorithms"> <meta property="og:description" content=" The paper proposes a two-phase classification method for detecting anomalies in network traffic, aiming to tackle the challenges of imbalance and feature selection. The study uses Information Gain to select relevant features and evaluates its performance on the CICIDS-2018 dataset with various classifiers. Results indicate that the ensemble classifier achieved the highest accuracy, precision, and recall. The proposed method addresses challenges in intrusion detection and highlights the effectiveness of ensemble classifiers in improving anomaly detection accuracy. Also, the quantity of pertinent characteristics chosen by Information Gain has a considerable impact on the F1-score and detection accuracy. Specifically, the Ensemble Learning achieved the highest accuracy of 98.36% and F1-score of 97.98% using the relevant selected features."/>   <meta name="twitter:card" content="Proceedings" /> <meta name="twitter:site" content="AIRCC" /> <meta name="twitter:title" content="Intrusion Detection System(IDS) Development Using Tree-Based Machine Learning Algorithms" /> <meta name="twitter:description" content=" The paper proposes a two-phase classification method for detecting anomalies in network traffic, aiming to tackle the challenges of imbalance and feature selection. The study uses Information Gain to select relevant features and evaluates its performance on the CICIDS-2018 dataset with various classifiers. Results indicate that the ensemble classifier achieved the highest accuracy, precision, and recall. The proposed method addresses challenges in intrusion detection and highlights the effectiveness of ensemble classifiers in improving anomaly detection accuracy. Also, the quantity of pertinent characteristics chosen by Information Gain has a considerable impact on the F1-score and detection accuracy. Specifically, the Ensemble Learning achieved the highest accuracy of 98.36% and F1-score of 97.98% using the relevant selected features."/> <meta name="twitter:image" content="https://airccse.org/img/aircc-logo1.jpg" />   <meta name="google-site-verification" content="t8rHIcM8EfjIqfQzQ0IdYIiA9JxDD0uUZAitBCzsOIw" /> <meta name="yandex-verification" content="e3d2d5a32c7241f4" />  <style type="text/css"> a{ color:white; text-decoration:none; } ul li a{ font-weight:bold; color:#000; list-style:none; text-decoration:none; size:10px;} .imagess { height:90px; text-align:left; margin:0px 5px 2px 8px; float:right; border:none; } #left p { font-family:CALIBRI; font-size:0.90pc; margin-left: 20px; } .right { margin-right: 20px; } #button{ float: left; font-size: 17px; margin-left: 10px; height: 28px; width: 100px; background-color: #1e86c6; } </style> <link rel="icon" type="image/ico" href="../fav.ico"/> <link rel="stylesheet" type="text/css" href="../current.css" /> </head> <body> <div id="wap"> <div id="page"> <div id="top"> <table width="100%" cellspacing="0" cellpadding="0" > <tr><td colspan="3" valign="top"><img src="../top1.gif" /></td></tr> </table> </div> <div id="menu"> <a href="http://airccse.org/journal/ijcnc.html">Home</a> <a href="http://airccse.org/journal/j2editorial.html">Editorial</a> <a href="http://airccse.org/journal/j2paper.html">Submission</a> <a href="http://airccse.org/journal/j2indexing.html">Indexing</a> <a href="http://airccse.org/journal/j2special.html">Special Issue</a> <a href="http://airccse.org/journal/j2contact.html">Contacts</a> <a href="http://airccse.org" target="_blank">AIRCC</a></div> <div id="content"> <div id="left"> <h2>Volume 15, Number 4</h2> <h4 style="text-align:center;height:auto"><a>Intrusion Detection System(IDS) Development Using Tree-Based Machine Learning Algorithms</a></h4> <h3>  Authors</h3> <p class="#left">Witcha Chimphlee and Siriporn Chimphlee, Suan Dusit University, Thailand </p> <h3>  Abstract</h3> <p class="#left right" style="text-align:justify">The paper proposes a two-phase classification method for detecting anomalies in network traffic, aiming to tackle the challenges of imbalance and feature selection. The study uses Information Gain to select relevant features and evaluates its performance on the CICIDS-2018 dataset with various classifiers. Results indicate that the ensemble classifier achieved the highest accuracy, precision, and recall. The proposed method addresses challenges in intrusion detection and highlights the effectiveness of ensemble classifiers in improving anomaly detection accuracy. Also, the quantity of pertinent characteristics chosen by Information Gain has a considerable impact on the F1-score and detection accuracy. Specifically, the Ensemble Learning achieved the highest accuracy of 98.36% and F1-score of 97.98% using the relevant selected features. </p> <h3>  Keywords</h3> <p class="#left right" style="text-align:justify">Intrusion Detection System, Anomaly Detection,Imbalance Data, Feature Selection, CICIDS-2018 dataset </p><br> <button type="button" id="button"><a target="blank" href="/ijcnc/V15N4/15423cnc06.pdf">Full Text</a></button>   <button type="button" id="button"><a href="http://airccse.org/journal/ijc2023.html">Volume 15</a></button> <br><br><br><br><br> </div> <div id="right"> <div class="menu_right"> <ul> <li><a href="http://airccse.org/journal/jcnc_arch.html">Archives</a></li> </ul> </div><br /> <p align="center"> </p> <p align="center"> </p> </div> <div class="clear"></div> <div id="footer"><table width="100%" ><tr><td height="25" colspan="2"><br /><p align="center">® All Rights Reserved - AIRCC</p></td></table> </div> </div> </div> </div> </body> </html>