MalScore: A Quality Assessment Framework for Visual Malware Datasets Using No-Reference Image Quality Metrics

MalScore: A Quality Assessment Framework for Visual Malware Datasets Using No-Reference Image Quality Metrics | 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 MalScore: A Quality Assessment Framework for Visual Malware Datasets Using No-Reference Image Quality Metrics Jakub Czaplicki, Mohamed Rahouti, Abdellah Chehri, Thaier Hayajneh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7160738/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 Internet has been progressively more integrated into daily life through its evolutionary stages, ranging from web1.0 to the current development of web3.0. These continued integrations broaden the attack surface that cybercriminals aim to exploit. The prevalence of cybercrimes, particularly malware attacks, has become increasingly sophisticated and made more accessible through dark web marketplaces. Including artificial intelligence (AI) within anti-virus solutions has challenged the traditional dichotomy of malware detection schemes, offering more accurate and holistic detection capabilities. Research has shown that transforming malware files into textured images offers resistance to obfuscation and the potential to detect zero-days. This paper explores the application of image quality assessment (IQA) techniques in enhancing visual malware dataset curation. We propose a novel framework that applies a no-reference IQA algorithm to evaluate current datasets and offer guidance in future dataset curation. We use datasets of various popularities and sizes, applying a standardized framework to assess and compare their effectiveness based on multiple metrics. These metrics raised awareness of malware's continuous evolution and were derived from a need for robust detection mechanisms. A proposed MalScore formula and framework are applied to facilitate the evaluation and ranking of datasets. This work bridges the gap between IQA and visual malware detection to set the foundation for future work to further unite the two research fields. The evaluation findings demonstrate that the proposed framework effectively distinguishes between datasets, highlighting strengths and areas for improvement. Visual malware detection image quality assessment synthetic dataset generation 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. 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-7160738","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":509865808,"identity":"cdd6b533-50bd-4891-a5aa-23e2bac9a9f6","order_by":0,"name":"Jakub Czaplicki","email":"","orcid":"","institution":"Fordham University","correspondingAuthor":false,"prefix":"","firstName":"Jakub","middleName":"","lastName":"Czaplicki","suffix":""},{"id":509865809,"identity":"fa0c517a-d90c-4fc5-a76d-fafa424d04d8","order_by":1,"name":"Mohamed Rahouti","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYHACNhDBw898gIGBtwEsYkCMFgMeybYEErUwGBwjVovu7MPPHnxs+yNjfIz52Ie3O+yiGdibt0ng02J2Ls3ccGabAY/ZMbbkmXPPJOc28Bwrw6/lDIOZNC9Iy/0eY2beNubcBokcMwJa2L+BtRi38YC01Oc2yL8hpIUHYosBG1jLYaAtPAS1lEnOOGfMIwH0C+PctuO5bTxpxRYEHLZN4kOZnD1/G/Nhhrdt1bn97Ic33sCnBROwkaZ8FIyCUTAKRgE2AABg3UD2FjxUtAAAAABJRU5ErkJggg==","orcid":"","institution":"Fordham University","correspondingAuthor":true,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Rahouti","suffix":""},{"id":509865810,"identity":"9b727e78-efc4-49eb-8da1-bb7e722317c4","order_by":2,"name":"Abdellah Chehri","email":"","orcid":"","institution":"Royal Military College of Canada","correspondingAuthor":false,"prefix":"","firstName":"Abdellah","middleName":"","lastName":"Chehri","suffix":""},{"id":509865811,"identity":"7b95446e-cc2f-49d7-b0d4-8d4e9acf6496","order_by":3,"name":"Thaier Hayajneh","email":"","orcid":"","institution":"Fordham University","correspondingAuthor":false,"prefix":"","firstName":"Thaier","middleName":"","lastName":"Hayajneh","suffix":""}],"badges":[],"createdAt":"2025-07-18 21:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7160738/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7160738/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91825502,"identity":"2e3c2fa5-8be9-4cf5-a1aa-220860506b1d","added_by":"auto","created_at":"2025-09-22 08:31:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":792711,"visible":true,"origin":"","legend":"","description":"","filename":"MalScore.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7160738/v1_covered_e2eb47f8-c1c2-4da7-b532-476b99368926.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"MalScore: A Quality Assessment Framework for Visual Malware Datasets Using No-Reference Image Quality Metrics","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Visual malware detection, image quality assessment, synthetic dataset generation","lastPublishedDoi":"10.21203/rs.3.rs-7160738/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7160738/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The Internet has been progressively more integrated into daily life through its evolutionary stages, ranging from web1.0 to the current development of web3.0. These continued integrations broaden the attack surface that cybercriminals aim to exploit. The prevalence of cybercrimes, particularly malware attacks, has become increasingly sophisticated and made more accessible through dark web marketplaces. Including artificial intelligence (AI) within anti-virus solutions has challenged the traditional dichotomy of malware detection schemes, offering more accurate and holistic detection capabilities. Research has shown that transforming malware files into textured images offers resistance to obfuscation and the potential to detect zero-days. This paper explores the application of image quality assessment (IQA) techniques in enhancing visual malware dataset curation. We propose a novel framework that applies a no-reference IQA algorithm to evaluate current datasets and offer guidance in future dataset curation. 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