CINXE.COM

<!DOCTYPE html> <html xmlns="http://www.w3.org/1999/xhtml"> <head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"> <title>Machine Learning Based Ensemble Classifier for Android Malware Detection</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="Machine Learning Based Ensemble Classifier for Android Malware Detection"> <meta name="description" content="Malware problem has infiltrated into every aspect of cyber space including Android mobiles. Due to proliferation of Android applications and widespread usage of smartphones,malware problem is causing significant damage to mobile users and application vendors. With the emergence of Artificial Intelligence (AI), machine learning (ML) models are widely used for detection of Android malware. However, many of the existing methods focused on static or dynamic data to train classifiers for malware detection. In this paper, we propose an ensemble model with intelligent methods that are empirically selected. Only the malware detection models with highest accuracy arechosen to be part of stacking ensemble model. An algorithm named Stacking Ensemble for Automatic Android Malware Detection (SE-AAMD)is proposed and implemented. We made three experiments with the same algorithm but three different datasets reflecting features obtained through different modus operandi. Each dataset is found to have influence on the performance of the models. However, in all experiments, the ensemble approach showed highest performance. The proposed method can be used in improving security for Android devices and applications."/> <meta name="keywords" content="Artificial Intelligence, Machine Learning, Anomaly Detection, Android Malware Detection "/>  <meta name="dc.title" content="Machine Learning Based Ensemble Classifier for Android Malware Detection "> <meta name="citation_authors" content="P Sumalatha"> <meta name="citation_authors" content=" G.S. Mahalakshmi"> <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.2023.15407"> <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=" Malware problem has infiltrated into every aspect of cyber space including Android mobiles. Due to proliferation of Android applications and widespread usage of smartphones,malware problem is causing significant damage to mobile users and application vendors. With the emergence of Artificial Intelligence (AI), machine learning (ML) models are widely used for detection of Android malware. However, many of the existing methods focused on static or dynamic data to train classifiers for malware detection. In this paper, we propose an ensemble model with intelligent methods that are empirically selected. Only the malware detection models with highest accuracy arechosen to be part of stacking ensemble model. An algorithm named Stacking Ensemble for Automatic Android Malware Detection (SE-AAMD)is proposed and implemented. We made three experiments with the same algorithm but three different datasets reflecting features obtained through different modus operandi. Each dataset is found to have influence on the performance of the models. However, in all experiments, the ensemble approach showed highest performance. The proposed method can be used in improving security for Android devices and applications. <meta name="dc.subject" content="Artificial Intelligence"> <meta name="dc.subject" content="Machine Learning"> <meta name="dc.subject" content="Anomaly Detection"> <meta name="dc.subject" content="Android Malware Detection"> <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="111">   <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="P Sumalatha and G.S. Mahalakshmi "> <meta name="citation_title" content="Machine Learning Based Ensemble Classifier for Android Malware Detection"> <meta name="citation_online_date" content="2023/07/30"> <meta name="citation_issue" content="15"> <meta name="citation_firstpage" content="111"> <meta name="citation_authors" content="P Sumalatha"> <meta name="citation_authors" content=" G.S. Mahalakshmi"> <meta name="citation_doi" content="10.5121/ijcnc.2023.15407"> <meta name="citation_abstract_html_url" content="https://aircconline.com/abstract/ijcnc/v15n4/15423cnc07.html"> <meta name="citation_pdf_url" content="https://aircconline.com/ijcnc/V15N4/15423cnc07.pdf">   <meta property="og:site_name" content="AIRCC" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://aircconline.com/abstract/ijcnc/v15n4/15423cnc07.html"> <meta property="og:title" content="Machine Learning Based Ensemble Classifier for Android Malware Detection"> <meta property="og:description" content=" Malware problem has infiltrated into every aspect of cyber space including Android mobiles. Due to proliferation of Android applications and widespread usage of smartphones,malware problem is causing significant damage to mobile users and application vendors. With the emergence of Artificial Intelligence (AI), machine learning (ML) models are widely used for detection of Android malware. However, many of the existing methods focused on static or dynamic data to train classifiers for malware detection. In this paper, we propose an ensemble model with intelligent methods that are empirically selected. Only the malware detection models with highest accuracy arechosen to be part of stacking ensemble model. An algorithm named Stacking Ensemble for Automatic Android Malware Detection (SE-AAMD)is proposed and implemented. We made three experiments with the same algorithm but three different datasets reflecting features obtained through different modus operandi. Each dataset is found to have influence on the performance of the models. However, in all experiments, the ensemble approach showed highest performance. The proposed method can be used in improving security for Android devices and applications."/>   <meta name="twitter:card" content="Proceedings" /> <meta name="twitter:site" content="AIRCC" /> <meta name="twitter:title" content="Machine Learning Based Ensemble Classifier for Android Malware Detection" /> <meta name="twitter:description" content=" Malware problem has infiltrated into every aspect of cyber space including Android mobiles. Due to proliferation of Android applications and widespread usage of smartphones,malware problem is causing significant damage to mobile users and application vendors. With the emergence of Artificial Intelligence (AI), machine learning (ML) models are widely used for detection of Android malware. However, many of the existing methods focused on static or dynamic data to train classifiers for malware detection. In this paper, we propose an ensemble model with intelligent methods that are empirically selected. Only the malware detection models with highest accuracy arechosen to be part of stacking ensemble model. An algorithm named Stacking Ensemble for Automatic Android Malware Detection (SE-AAMD)is proposed and implemented. We made three experiments with the same algorithm but three different datasets reflecting features obtained through different modus operandi. Each dataset is found to have influence on the performance of the models. However, in all experiments, the ensemble approach showed highest performance. The proposed method can be used in improving security for Android devices and applications."/> <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>Machine Learning Based Ensemble Classifier for Android Malware Detection</a></h4> <h3>  Authors</h3> <p class="#left">P Sumalatha<sup>1</sup> and G.S. Mahalakshmi<sup>2</sup>, <sup>1</sup>Bhoj Reddy Engineering College for Women, India, <sup>2</sup>Anna University, India </p> <h3>  Abstract</h3> <p class="#left right" style="text-align:justify">Malware problem has infiltrated into every aspect of cyber space including Android mobiles. Due to proliferation of Android applications and widespread usage of smartphones,malware problem is causing significant damage to mobile users and application vendors. With the emergence of Artificial Intelligence (AI), machine learning (ML) models are widely used for detection of Android malware. However, many of the existing methods focused on static or dynamic data to train classifiers for malware detection. In this paper, we propose an ensemble model with intelligent methods that are empirically selected. Only the malware detection models with highest accuracy arechosen to be part of stacking ensemble model. An algorithm named Stacking Ensemble for Automatic Android Malware Detection (SE-AAMD)is proposed and implemented. We made three experiments with the same algorithm but three different datasets reflecting features obtained through different modus operandi. Each dataset is found to have influence on the performance of the models. However, in all experiments, the ensemble approach showed highest performance. The proposed method can be used in improving security for Android devices and applications. </p> <h3>  Keywords</h3> <p class="#left right" style="text-align:justify">Artificial Intelligence, Machine Learning, Anomaly Detection, Android Malware Detection </p><br> <button type="button" id="button"><a target="blank" href="/ijcnc/V15N4/15423cnc07.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>