Malware Detection in Embedded Devices using Artificial Hardware Immunity

Malware Detection in Embedded Devices using Artificial Hardware Immunity | 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 Malware Detection in Embedded Devices using Artificial Hardware Immunity Farhath Zareen, Mateus Augusto Fernandes Amador, Robert Karam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2758367/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Aug, 2025 Read the published version in Journal of Hardware and Systems Security → Version 1 posted 10 You are reading this latest preprint version Abstract With the rapid proliferation of IoT devices and its growing usage in safety-critical systems, securing these devices from malicious attacks has become increasingly challenging. Due to the resource-constrained nature of IoT devices, real-time software-based malware detection is difficult or infeasible. Alternatively, a promising approach is utilizing hardware malware detection techniques. In this paper, we introduce a novel Hardware Immune System (HWIS), a stand-alone, hardware-supported malware detection approach for microprocessors that leverages Artificial Immune Systems for detecting botnet activity. This technique is suitable for low-power, resource constrained and network facing embedded devices. The proposed model is capable of detecting botnet behavior with an accuracy of 96.7%, false negative rate of 6.5%, and F1-score of 0.96. We implemented and simulated the proposed architecture using 32nm low power PTM SPICE models and the Synopsys 32nm EDK and found the power and area overhead to be 2.57% and 5.25%, respectively, with no impact on delay, using a 28nm RISC-V CPU as a baseline. IoT Security Artificial Immune Systems Machine Learning Hardware Malware Detectors Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Aug, 2025 Read the published version in Journal of Hardware and Systems Security → Version 1 posted Reviews received at journal 29 Jul, 2024 Reviewers agreed at journal 15 Jul, 2024 Reviewers agreed at journal 13 Jul, 2024 Reviews received at journal 18 May, 2023 Reviewers agreed at journal 27 Apr, 2023 Reviewers agreed at journal 21 Apr, 2023 Reviewers invited by journal 19 Apr, 2023 Editor assigned by journal 11 Apr, 2023 Submission checks completed at journal 11 Apr, 2023 First submitted to journal 30 Mar, 2023 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-2758367","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":190856538,"identity":"4dc27343-1204-4caf-adc5-7f789ef10592","order_by":0,"name":"Farhath Zareen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYBAC9gYILcfAwPiAgYGNCC08ByC0MQMDswFpWhIbiNfCf/jZhw8Vd9I3nD/MwPCh7DARWhiOGc+cceZZ7oYbyQyMM84RocWescGYmbftMFAL/wEQgwhbmNk/M//9dzjdAOgw5r9EaWHjMWZmbDicYHAgmYGZkSgtPDzFjD3HDhvOBPrlYM+5dCK08B/fzPCj5rA83/nDjA9+lFkT1oICDpCofhSMglEwCkYBLgAAlD46JaHc/K4AAAAASUVORK5CYII=","orcid":"","institution":"University of South Florida","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Farhath","middleName":"","lastName":"Zareen","suffix":""},{"id":190856539,"identity":"7f5cff9c-dced-4748-bf1d-648ccfe9f82a","order_by":1,"name":"Mateus Augusto Fernandes Amador","email":"","orcid":"","institution":"University of South Florida","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mateus","middleName":"Augusto Fernandes","lastName":"Amador","suffix":""},{"id":190856540,"identity":"0bfcee3f-d2c0-4385-857d-7b48dc9528bc","order_by":2,"name":"Robert Karam","email":"","orcid":"","institution":"University of South Florida","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Karam","suffix":""}],"badges":[],"createdAt":"2023-03-30 21:59:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2758367/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2758367/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s41635-025-00166-w","type":"published","date":"2025-08-13T15:57:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89310658,"identity":"a48b8818-fdb3-404e-a4d9-849aabf17d4f","added_by":"auto","created_at":"2025-08-18 16:09:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":886711,"visible":true,"origin":"","legend":"","description":"","filename":"JournalofHardwareandSystemsSecurtiyHaSS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2758367/v1_covered_1ce0114e-e9bc-4675-9776-11122e8d8236.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Malware Detection in Embedded Devices using Artificial Hardware Immunity","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-hardware-and-systems-security","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hass","sideBox":"Learn more about [Journal of Hardware and Systems Security](https://link.springer.com/journal/41635)","snPcode":"41635","submissionUrl":"https://submission.nature.com/new-submission/41635/3","title":"Journal of Hardware and Systems Security","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"IoT Security , Artificial Immune Systems , Machine Learning , Hardware Malware Detectors","lastPublishedDoi":"10.21203/rs.3.rs-2758367/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2758367/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"With the rapid proliferation of IoT devices and its growing usage in safety-critical systems, securing these devices from malicious attacks has become increasingly challenging. 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