Smali opcode based Android Malware detection and Obfuscation Identification

Smali opcode based Android Malware detection and Obfuscation Identification | 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 Smali opcode based Android Malware detection and Obfuscation Identification Abhishek Anand, Jyoti Prakash Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3493657/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 Android platform's open-source nature makes it a prime target for attackers seeking to exploit vulnerabilities. The practice of reverse engineering in Android applications further increases this vulnerability, creating a lucrative ground for exploitation and attack. Malware developers use various obfuscation techniques to protect applications from reverse engineering attempts. These same obfuscation techniques are utilized by malware creators to hide malicious code within the application's structure. Obfuscation introduces useless code and concealed features during feature extraction, making it difficult for conventional malware analysis methods to recognise the application and resulting in a high rate of false negatives. To address this, this paper introduces an innovative Smali opcode-based model, specifically designed to address the complexity of obfuscation techniques during both binary and familial classification. The core objective is to design a lightweight model capable of classifying malware and benign applications, alongside robust familial classification. Moreover, the model is also equipped to identify the specific obfuscation technique employed in a given malware application. We have meticulously implemented and rigorously evaluated the proposed model using two distinct datasets encompassing obfuscated and non-obfuscated samples. The experimental findings affirm the model's performance, surpassing existing state-of-the-art Android malware classifiers. Notably, the model achieves an impressive binary classification accuracy of 99.4%. Android Malware Smali Opcodes Obfuscation Malware Families 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. 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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-3493657","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":243099028,"identity":"4cef5aae-81a4-4c2d-8c8b-c8a6943d08d4","order_by":0,"name":"Abhishek Anand","email":"data:image/png;base64,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","orcid":"","institution":"National Institute of Technology Patna","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Abhishek","middleName":"","lastName":"Anand","suffix":""},{"id":243099029,"identity":"cf6af611-7a75-44ad-a3bf-cdfc6288a247","order_by":1,"name":"Jyoti Prakash Singh","email":"","orcid":"","institution":"National Institute of Technology Patna","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jyoti","middleName":"Prakash","lastName":"Singh","suffix":""}],"badges":[],"createdAt":"2023-10-26 08:14:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3493657/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3493657/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77586215,"identity":"fc46e0a3-a9ef-4b0c-a947-69a78035faaf","added_by":"auto","created_at":"2025-03-03 10:38:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1055944,"visible":true,"origin":"","legend":"","description":"","filename":"SmaliopcodebasedAndroidMalwaredetectionandObfuscationIdentification.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3493657/v1_covered_28ef912d-85de-4ff2-baf4-4493ba2bde4c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Smali opcode based Android Malware detection and Obfuscation Identification","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":"Android Malware, Smali,Opcodes, Obfuscation, Malware Families","lastPublishedDoi":"10.21203/rs.3.rs-3493657/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3493657/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The Android platform's open-source nature makes it a prime target for attackers seeking to exploit vulnerabilities. The practice of reverse engineering in Android applications further increases this vulnerability, creating a lucrative ground for exploitation and attack. Malware developers use various obfuscation techniques to protect applications from reverse engineering attempts. These same obfuscation techniques are utilized by malware creators to hide malicious code within the application's structure. Obfuscation introduces useless code and concealed features during feature extraction, making it difficult for conventional malware analysis methods to recognise the application and resulting in a high rate of false negatives. To address this, this paper introduces an innovative Smali opcode-based model, specifically designed to address the complexity of obfuscation techniques during both binary and familial classification. The core objective is to design a lightweight model capable of classifying malware and benign applications, alongside robust familial classification. Moreover, the model is also equipped to identify the specific obfuscation technique employed in a given malware application. We have meticulously implemented and rigorously evaluated the proposed model using two distinct datasets encompassing obfuscated and non-obfuscated samples. The experimental findings affirm the model's performance, surpassing existing state-of-the-art Android malware classifiers. 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