Android Malware Detection System Based on Ensemble Learning

Android Malware Detection System Based on Ensemble Learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Android Malware Detection System Based on Ensemble Learning Orieb AbuAlghanam, Hadeel Alazzam, Mohammad Qatawneh, Omar Aladwan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2521341/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The rapid advancement of smartphones, as well as their widespread use, has resulted in a significant increase in new security concerns. Malware’s covert techniques make signature-based anti-virus/anti-malware solutions difficult to detect. The features used in such solutions are extracted from static or dynamic analysis. In this paper, an Android malware detection system has been proposed. It consists of two main subsystems that work in parallel, one has been trained for benign labeled apps while the second one has been trained on malware labeled apps. Each subsystem is based on an ensemble approach that consists of OC-SVM, LOF, and modified isolation forest (M-iForest) classifiers. Each subsystem used three one-class classifiers to take the decision in each subsystem independently. Moreover, each subsystem used both features that are extracted from static and dynamic malware analysis. The evaluation has been conducted based on two An-droid malware benchmark datasets which are DREBIN and CICAndMal2017. The proposed system achieved the highest accuracy compared to other related techniques; The accuracy for the DREBIN dataset was 98.7%, and 95.67% F-Score, while the accuracy for CICAndMal2017 was 98.99% and F-Score of 96.82%. CICAndMal2017 DREBIN Dataset Isolation Forest Malware Detection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2521341","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":171310643,"identity":"d4c217ad-a63f-4cdf-83d7-03261d40b125","order_by":0,"name":"Orieb AbuAlghanam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYHACNiC2gDA/8IDIBAYGCcJaIEoYZ5CshRmsA6QFH+BnYH72mKdGwq5/dvPTzTYyhxn42XMMGCwqcGuRbGAzN+Y5JpE8484xs9s5PIcZJHveGDBInMGtxeAAg5k0D5tEMsONBIgWgxtAWyTb8Glh/ybN808iWf5G+rfbFkAt9oS18JhJ87ZJ2AENN7vNALJFgoAWyWaecsO5fRIJhjdyym728KTzSJx5VnAAn1/42du3PXjzzcZe7kb6ths/e6zl+NuTNz6WwBNiDMwQKrEBRDL2MICj5jCBqAQDewj1A0IxfiBCyygYBaNgFIwYAABvgEnEHw4Q7QAAAABJRU5ErkJggg==","orcid":"","institution":"The University of Jordan","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Orieb","middleName":"","lastName":"AbuAlghanam","suffix":""},{"id":171310644,"identity":"d238c024-33a0-46fd-b09a-35e37933d654","order_by":1,"name":"Hadeel Alazzam","email":"","orcid":"","institution":"Al-Balqa Applied University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hadeel","middleName":"","lastName":"Alazzam","suffix":""},{"id":171310645,"identity":"8fbe58ee-033f-4edd-a584-62de2c99f1c5","order_by":2,"name":"Mohammad Qatawneh","email":"","orcid":"","institution":"The University of Jordan","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Qatawneh","suffix":""},{"id":171310646,"identity":"068114f3-5c59-4afe-b80f-5dd0cf745322","order_by":3,"name":"Omar Aladwan","email":"","orcid":"","institution":"Al-Ahliyya Amman University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Omar","middleName":"","lastName":"Aladwan","suffix":""},{"id":171310647,"identity":"6bbee3d7-2f33-4420-a829-a43ee90a96ee","order_by":4,"name":"Mohammad A. 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Malware’s covert techniques make signature-based anti-virus/anti-malware solutions difficult to detect. The features used in such solutions are extracted from static or dynamic analysis. In this paper, an Android malware detection system has been proposed. It consists of two main subsystems that work in parallel, one has been trained for benign labeled apps while the second one has been trained on malware labeled apps. Each subsystem is based on an ensemble approach that consists of OC-SVM, LOF, and modified isolation forest (M-iForest) classifiers. Each subsystem used three one-class classifiers to take the decision in each subsystem independently. Moreover, each subsystem used both features that are extracted from static and dynamic malware analysis. The evaluation has been conducted based on two An-droid malware benchmark datasets which are DREBIN and CICAndMal2017. 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