Remedial Actions for Self-Healing in Cyber-Physical Systems

Remedial Actions for Self-Healing in Cyber-Physical Systems | 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 Remedial Actions for Self-Healing in Cyber-Physical Systems Obinna Johnphill, Ali Safaa Sadiq, Omprakash Kaiwartya, Mohammed Adam Taheird This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7030947/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 Building upon our previous work on theLog Intelligence and Self-Healing System (LISH), whichutilised CountVectorizer and Multinomial Naive Bayes(MNB) for multi-platform anomaly detection, this pa-per advances the self-healing paradigm by operational-ising the autonomous execution of remedial actions fol-lowing anomaly classification. We introduce CROSS:Cross-platform Remediation and Observability Self-Healing System, a novel framework designed to auto-mate post-detection recovery tasks such as system up-dates, service restarts, device reboots, disk clean-up,and configuration enforcement across Android, Linux,macOS, and Windows environments. CROSS processespre-classified logs to detect elevated error and warninglevels per system and initiates platform-specific cor-rective actions through a unified handler mechanism.Upon breach, it triggers remediation routines tailoredto the target environment, ensuring contextual appro-priateness and minimal manual oversight. Prometheusmetrics are integrated to expose fine-grained teleme-try on detected anomalies and remedial actions, en-hancing observability and enabling external policy en-forcement or audit. Experimental validation across het-erogeneous cyber-physical system (CPS) environmentsdemonstrates significant improvements in recovery la-tency and fault tolerance. This research reinforces theimperative of automating the post-detection responsephase in security-focused self-healing systems. Futurework will address risk-aware remedial action selectionand integrate secure execution constraints to mitigate the threat surface introduced by autonomous system-level interventions. self-healing cyber-physical systems remedial actions cloud environments automatic failover service restart system resilience 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. 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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-7030947","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":516578840,"identity":"f50bbd89-57c5-42d4-a968-d713236ce213","order_by":0,"name":"Obinna Johnphill","email":"","orcid":"","institution":"Nottingham Trent University","correspondingAuthor":false,"prefix":"","firstName":"Obinna","middleName":"","lastName":"Johnphill","suffix":""},{"id":516578841,"identity":"e01bd22f-1292-4ea6-be48-23d98b8d6ef4","order_by":1,"name":"Ali Safaa Sadiq","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIie3RsQrCMBCA4QuBdrm2awSxr5DMBX2VilAXcRYUDAi6uQs+TKFglz6A4iSCk4KTdBKvQ3EQ0o4O+YcbAh9JOACb7R9zmabJEVxdHzkNhNcEU6ZbknqKuCUJVlyzchF1/dMjvyL0QxBJbCQiY5rjYYyd83S4QhgpLZLUfE1FwMlQnieKCI9BjLVRhERY+SZyKiqybCaSCHhrIkesSEak4WGKSOZt6S/FRO32MldrvMVG0ss3l0v5igZ+XsjnfTYPAzeRRlKVfqdsXuQPtNlsNttvHwcPPs5/TAUcAAAAAElFTkSuQmCC","orcid":"","institution":"Nottingham Trent University","correspondingAuthor":true,"prefix":"","firstName":"Ali","middleName":"Safaa","lastName":"Sadiq","suffix":""},{"id":516578842,"identity":"6296647b-4140-4b06-afdd-f320430f1f9d","order_by":2,"name":"Omprakash Kaiwartya","email":"","orcid":"","institution":"Nottingham Trent University","correspondingAuthor":false,"prefix":"","firstName":"Omprakash","middleName":"","lastName":"Kaiwartya","suffix":""},{"id":516578843,"identity":"7b77a66d-561f-4610-a2c3-a9a7e830204e","order_by":3,"name":"Mohammed Adam Taheird","email":"","orcid":"","institution":"University of Zalingei","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"Adam","lastName":"Taheird","suffix":""}],"badges":[],"createdAt":"2025-07-02 15:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7030947/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7030947/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91949462,"identity":"8c1da68a-4469-4430-97b0-b90321e4c969","added_by":"auto","created_at":"2025-09-23 06:21:35","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6494,"visible":true,"origin":"","legend":"","description":"","filename":"904ca6f99e53406cbbfbf1a79907cd83.json","url":"https://assets-eu.researchsquare.com/files/rs-7030947/v1/b6003ad580637f4e5c8002a4.json"},{"id":92717405,"identity":"c86068b0-1261-48f0-b360-ec3270042dcd","added_by":"auto","created_at":"2025-10-03 12:46:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1597332,"visible":true,"origin":"","legend":"","description":"","filename":"CROSSSourceFiles.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7030947/v1_covered_3536704d-4658-40ba-8f0a-8cf54bcf3d93.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Remedial Actions for Self-Healing in Cyber-Physical Systems","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":"self-healing, cyber-physical systems, remedial actions, cloud environments, automatic failover, service restart, system resilience","lastPublishedDoi":"10.21203/rs.3.rs-7030947/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7030947/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Building upon our previous work on theLog Intelligence and Self-Healing System (LISH), whichutilised CountVectorizer and Multinomial Naive Bayes(MNB) for multi-platform anomaly detection, this pa-per advances the self-healing paradigm by operational-ising the autonomous execution of remedial actions fol-lowing anomaly classification. 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