OHMN: Android Malware Detection Method Using Opcode Highway Memory Network

OHMN: Android Malware Detection Method Using Opcode Highway Memory Network | 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 OHMN: Android Malware Detection Method Using Opcode Highway Memory Network M. Karthika, M. Anusha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4114338/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 Malware developed for Android devices has recently showed a preference for rapid replication, which speeds up the process of stealing sensitive data. By reusing code, hackers introduce potentially harmful modifications. In this study, we suggest using a deep learning-based network called the "Opcode highway memory network" (OHMN), which may identify versions of malware that are related to one another without requiring the analyst to have any specialized understanding in mathematics or methodology. Truth interference fuzzy clustering is a way to find features using software birth marking of malcode sequences, and sequence patch normalization may be used to standardize the input data. Malware samples are clustered, and that information is sent into the OHMN process that looks for different strains of malware. The Android malware detection system has been shown to operate well via both observation and experimentation. General testing was performed on the Mal Radar dataset, an android malware dataset obtained from the zenodo under python environment. As shown by the simulation results, the proposed approach outperformed the state-of-the-art algorithm. Android malware sequence patch normalization Truth interference fuzzy clustering Opcode highway memory network 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-4114338","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":282203872,"identity":"234c787a-3684-48bb-94a4-cd305b7aa7e2","order_by":0,"name":"M. 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