A Novel Wavelet-Based Model For Android Malware Detection Utilizing System Calls Features

A Novel Wavelet-Based Model For Android Malware Detection Utilizing System Calls Features | 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 A Novel Wavelet-Based Model For Android Malware Detection Utilizing System Calls Features Akram CHHAYBI, Saiida LAZAAR This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4969059/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Jun, 2025 Read the published version in Journal of Network and Systems Management → Version 1 posted 4 You are reading this latest preprint version Abstract Security is a significant concern in the realm of Android platforms, given the ability to allow installations from unverified sources exposing devices to possible malware infiltration and malicious attacks. Malware classification remains a challenging task in the field of mobile malware detection, where dynamic analysis plays a central role in intrusion detection systems (IDS). This process aims real-time examination of application behavior enabling extraction of dynamic patterns exhibited by malware. System calls, provided by the device’s operating system and accessible from user applications, are deployed in machine learning methods for malware detection. The performance of machine learning-based detection algorithms requires improving the feature selection within system calls. This work suggests an original approach based on wavelets to enhance detection of a wide spectrum of Android malwares, such as adware, riskware, banking and SMS. The present methodology uses the Chi-Square test for feature selection, and Haar wavelet for converting selected attributes into wavelet coefficients. In our experiments, we use several machine-learning classifiers such as Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Neural Network (NN). To assess the performance of these classifiers, we employ evaluation metrics in terms of Accuracy, Recall, F-Score, and Precision. Among the selected classifiers, the Random Forest model coupled with wavelet feature selection provided the highest performance, achieving an accuracy rate of 99.99%. The results clearly demonstrate the effectiveness of our proposed model. Android IDS Machine learning Malware System calls Chi-Square Wavelet coefficients. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 Jun, 2025 Read the published version in Journal of Network and Systems Management → Version 1 posted Editorial decision: Revision requested 18 Nov, 2024 Editor assigned by journal 18 Nov, 2024 Submission checks completed at journal 27 Aug, 2024 First submitted to journal 24 Aug, 2024 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4969059","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":345927286,"identity":"350e9484-21a8-419a-8c48-a5083170b726","order_by":0,"name":"Akram CHHAYBI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIie2PsQrCMBRFGwKdhDoKgv6C4uinuCSLU+Pi0qFKprr4AR2k/oIuzimBuKR0Leigi6OgU8HFJINbrW6COcPjPriHx3Mci+UXaZjJkE4MBSpDSL9QTlIr4AsFnCO91CjeMjvfi/A48WKSMpzMRt5CKWWwq1Ra2WLQ9sVl2iomiOHdnsQcULCUh+ozueu0CeWYFn5PKUJlQCGIqpVu7sKHVtZGWQmyrlN6WeSaKxuj0JBs6pS+FO7QFxxv5RUxJBjZKiV990tHjuHBDzlO9oTfynBOkpynpzJ48/6LJtKTm8w+6Cs805t/VrZYLJa/4glH+GsOKSFw2QAAAABJRU5ErkJggg==","orcid":"","institution":"AbedlMalek Essaadi University","correspondingAuthor":true,"prefix":"","firstName":"Akram","middleName":"","lastName":"CHHAYBI","suffix":""},{"id":345927287,"identity":"383a3851-2d8b-415c-b5fa-109f4fcc72e6","order_by":1,"name":"Saiida LAZAAR","email":"","orcid":"","institution":"AbedlMalek Essaadi University","correspondingAuthor":false,"prefix":"","firstName":"Saiida","middleName":"","lastName":"LAZAAR","suffix":""}],"badges":[],"createdAt":"2024-08-24 12:04:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4969059/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4969059/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10922-025-09939-8","type":"published","date":"2025-06-12T15:57:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84726730,"identity":"6d0c4910-e165-4adb-b7c4-e8b96fa1741f","added_by":"auto","created_at":"2025-06-16 16:07:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":406355,"visible":true,"origin":"","legend":"","description":"","filename":"ANovelWaveletBasedModelForAndroidMalwareDetectionUtilizingSystemCallsFeatures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4969059/v1_covered_b1d8f8c0-b620-4a9b-889b-789a19bca2f4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Wavelet-Based Model For Android Malware Detection Utilizing System Calls Features","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-network-and-systems-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jons","sideBox":"Learn more about [Journal of Network and Systems Management](http://link.springer.com/journal/10922)","snPcode":"10922","submissionUrl":"https://submission.nature.com/new-submission/10922/3","title":"Journal of Network and Systems Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Android, IDS, Machine learning, Malware, System calls, Chi-Square, Wavelet coefficients.","lastPublishedDoi":"10.21203/rs.3.rs-4969059/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4969059/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Security is a significant concern in the realm of Android platforms, given the ability to allow installations from unverified sources exposing devices to possible malware infiltration and malicious attacks. Malware classification remains a challenging task in the field of mobile malware detection, where dynamic analysis plays a central role in intrusion detection systems (IDS). This process aims real-time examination of application behavior enabling extraction of dynamic patterns exhibited by malware. System calls, provided by the device’s operating system and accessible from user applications, are deployed in machine learning methods for malware detection. The performance of machine learning-based detection algorithms requires improving the feature selection within system calls. This work suggests an original approach based on wavelets to enhance detection of a wide spectrum of Android malwares, such as adware, riskware, banking and SMS. The present methodology uses the Chi-Square test for feature selection, and Haar wavelet for converting selected attributes into wavelet coefficients. 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