Explainable AI for Mobile Security: A Hybrid Approach to Android Malware Detection

Explainable AI for Mobile Security: A Hybrid Approach to Android Malware Detection | 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 Explainable AI for Mobile Security: A Hybrid Approach to Android Malware Detection Mohd Fozla Rabby This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7714083/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 rise in Android malware shows that mobile security needs better ways to find and understand threats. The goal of this study is to create a strong, clear framework for finding Android malware. We present a hybrid feature fusion methodology that integrates static metadata (permissions, intents), dynamic API requests (exceeding 23,000 behavioral indicators), and DEX image-based features derived from a convolutional neural network. The approach uses a dataset of 74,268 APK samples from a public repository. Of these, 18,440 are benign and 55,828 are malicious. The dataset is split into 70% for training, 10% for validation, and 20% for testing. Our results show that we got a perfect 100% accuracy on the test set, which is better than other methods. Explainable AI approaches find the most important factors that affect classification, like questionable permissions and API requests related to the network. These techniques make it easier for non-technical users to understand. This work is new because it combines multi-modal characteristics with interpretable AI to create a scalable, clear solution for finding Android malware in the real world. Android malware detection hybrid feature fusion explainable AI SHAP static analysis dynamic analysis DEX image features XGBoost mobile security interpretable machine learning 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-7714083","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":521427402,"identity":"d6c1d294-0723-4f95-93ed-648b9a21b737","order_by":0,"name":"Mohd Fozla Rabby","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIie2RsYrCQBCGxxPWZiDtSCS+giHgCb5M0phGra0kNqbxAZTzYQILsVn72AlpVQI2FiKOVlftaie4X7W7zMf8MwtgsXwkKAAmhO1Gwpfs+SQAjYrqef4ie0epzScBFOGLym+6zUsUFM1WZVlVSoKTZmJ/0CgtNY4DRIpSd9BtLgsJpMKGv9YoBMOui8Rd/oaijpUEKEC4umDkHFjpUJTsVP18ZaVtVOjRJSQeH8EFDtYxK6fYX2fES+ZZFipGX0WpfhZnlO+Ptyl/pSyrS973vI3MtRt78PM/Bp9riUHgkouxxGKxWL6aO2rzSxn5QAYQAAAAAElFTkSuQmCC","orcid":"","institution":"Khulna University","correspondingAuthor":true,"prefix":"","firstName":"Mohd","middleName":"Fozla","lastName":"Rabby","suffix":""}],"badges":[],"createdAt":"2025-09-25 14:38:33","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7714083/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7714083/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92375237,"identity":"d6a29cd8-79b0-42e8-a5eb-8300b8a1ee19","added_by":"auto","created_at":"2025-09-29 04:24:41","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2925,"visible":true,"origin":"","legend":"","description":"","filename":"41f51c3d3dad4932af7e95e7d98f093d.json","url":"https://assets-eu.researchsquare.com/files/rs-7714083/v1/9626b213815fcc762cceca00.json"},{"id":92375326,"identity":"b4688abe-a7da-4502-9d31-3310f06eb962","added_by":"auto","created_at":"2025-09-29 04:32:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1863674,"visible":true,"origin":"","legend":"","description":"","filename":"ExplainableAIforMobileSecurityAHybridApproachtoAndroidMalwareDetection.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7714083/v1/babe281bdabe9ab3332d31d6.pdf"},{"id":92375240,"identity":"a3c662f1-9231-4990-a95b-80dedf5f8945","added_by":"auto","created_at":"2025-09-29 04:24:41","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":35942,"visible":true,"origin":"","legend":"","description":"","filename":"cover.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7714083/v1/7130629ac0819c25ce8f9c42.pdf"},{"id":92375325,"identity":"afb31e66-fddf-431c-97ed-730823b22365","added_by":"auto","created_at":"2025-09-29 04:32:41","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":27471,"visible":true,"origin":"","legend":"","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7714083/v1/e47af591cb012698a47f3363.png"},{"id":92375242,"identity":"a7547f49-0f5a-4450-b1d5-e7a51beac264","added_by":"auto","created_at":"2025-09-29 04:24:41","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":358079,"visible":true,"origin":"","legend":"","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7714083/v1/6ceb8bd4f3a3176a4f525443.png"},{"id":92375238,"identity":"ef34f586-60a1-4b42-89b6-81364e96cc8b","added_by":"auto","created_at":"2025-09-29 04:24:41","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":40402,"visible":true,"origin":"","legend":"","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7714083/v1/9cf1881d40de0a5d4072a7e2.png"},{"id":92375244,"identity":"fa3fb41c-0d56-48c0-a178-3dbd67fce888","added_by":"auto","created_at":"2025-09-29 04:24:41","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":29295,"visible":true,"origin":"","legend":"","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-7714083/v1/5f8f842beeac1ba65199dff9.png"},{"id":92375245,"identity":"6a37c80e-58a7-4c9d-be24-43b6ef5dc38b","added_by":"auto","created_at":"2025-09-29 04:24:41","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":356963,"visible":true,"origin":"","legend":"","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-7714083/v1/8b536e28ed97514d012d35b6.png"},{"id":92375246,"identity":"8aef0424-7494-439a-84ff-d45e896c92b5","added_by":"auto","created_at":"2025-09-29 04:24:41","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1130558,"visible":true,"origin":"","legend":"","description":"","filename":"fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-7714083/v1/cff8d08448e84216f237edd6.png"},{"id":92375243,"identity":"0dc78ecb-6065-47b9-89eb-d8cc8442bbd0","added_by":"auto","created_at":"2025-09-29 04:24:41","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":63368,"visible":true,"origin":"","legend":"","description":"","filename":"41f51c3d3dad4932af7e95e7d98f093d1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7714083/v1/26717a792a57bb76dc88893e.xml"},{"id":92774841,"identity":"e07898bd-dc8e-445e-ae80-54e936c8779f","added_by":"auto","created_at":"2025-10-04 14:16:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":986336,"visible":true,"origin":"","legend":"","description":"","filename":"ExplainableAIforMobileSecurityAHybridApproachtoAndroidMalwareDetection.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7714083/v1_covered_645625c0-e9a5-45c2-a937-ea3ea5996ece.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Explainable AI for Mobile Security: A Hybrid Approach to Android Malware Detection","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Android malware detection, hybrid feature fusion, explainable AI, SHAP, static analysis, dynamic analysis, DEX image features, XGBoost, mobile security, interpretable machine learning","lastPublishedDoi":"10.21203/rs.3.rs-7714083/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7714083/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The rise in Android malware shows that mobile security needs better ways to find and understand threats. The goal of this study is to create a strong, clear framework for finding Android malware. We present a hybrid feature fusion methodology that integrates static metadata (permissions, intents), dynamic API requests (exceeding 23,000 behavioral indicators), and DEX image-based features derived from a convolutional neural network. The approach uses a dataset of 74,268 APK samples from a public repository. Of these, 18,440 are benign and 55,828 are malicious. The dataset is split into 70\\% for training, 10\\% for validation, and 20\\% for testing. Our results show that we got a perfect 100\\% accuracy on the test set, which is better than other methods. Explainable AI approaches find the most important factors that affect classification, like questionable permissions and API requests related to the network. These techniques make it easier for non-technical users to understand. This work is new because it combines multi-modal characteristics with interpretable AI to create a scalable, clear solution for finding Android malware in the real world.","manuscriptTitle":"Explainable AI for Mobile Security: A Hybrid Approach to Android Malware Detection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-29 04:24:36","doi":"10.21203/rs.3.rs-7714083/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"414c683c-59c6-4b2e-847e-e866da927938","owner":[],"postedDate":"September 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-04T14:08:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-29 04:24:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7714083","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7714083","identity":"rs-7714083","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}