A Systematic Analysis on the Use of AI Techniques in Industrial IoT DDoS Attacks Detection, Mitigation and Prevention

A Systematic Analysis on the Use of AI Techniques in Industrial IoT DDoS Attacks Detection, Mitigation and Prevention | 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 A Systematic Analysis on the Use of AI Techniques in Industrial IoT DDoS Attacks Detection, Mitigation and Prevention Mikiyas ALEMAYEHU, Mohamed Chahine GHANEM, Karim OUAZZANE, Hamza KHEDDAR, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6435716/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 Distributed Denial of Service (DDoS) attacks pose significant threats to Industrial Internet of Things (IIoT) environments, exacerbated by the resource constraints of IoT devices and the disruptive impact of such attacks. Conventional detection and prevention methods fall short of ensuring the availability and operational continuity required in industrial IIoT deployments. This article systematically analyses artificial intelligence (AI) techniques for detecting, preventing, and mitigating DDoS attacks in IIoT systems. We examine diverse AI-driven solutions, including machine learning (ML) and deep learning (DL) models, often integrated with traditional anomaly detection, signature-based systems, and blockchain technology. These hybrid approaches enhance real-time threat identification, adaptive defence mechanisms, and decentralized trust management, addressing the evolving sophistication of DDoS attacks. The study highlights AI’s potential to strengthen IIoT security and resilience, particularly in Critical National Infrastructures (CNIs), where uninterrupted operations are paramount. However, challenges such as computational overhead, model interpretability, and dataset scarcity in industrial settings remain critical barriers. Additionally, the dynamic IIoT topology and heterogeneous device ecosystems necessitate context-aware AI solutions. This analysis underscores the need for lightweight, explainable AI frameworks and collaborative defence strategies tailored to IIoT’s unique constraints. The paper identifies current research challenges and outlines future directions, emphasizing the integration of AI with emerging technologies like edge computing and federated learning to advance proactive, scalable DDoS defence mechanisms in industrial ecosystems Artificial Intelligence IoT IIoT Critical National Infrastructure Resilience DDoS Machine Learning Deep Learning Cyber security 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-6435716","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":444214429,"identity":"ce99e933-b97c-4d39-b9ea-d7e9174412e6","order_by":0,"name":"Mikiyas ALEMAYEHU","email":"","orcid":"","institution":"London Metropolitan University","correspondingAuthor":false,"prefix":"","firstName":"Mikiyas","middleName":"","lastName":"ALEMAYEHU","suffix":""},{"id":444214430,"identity":"121b262c-6c3c-41ff-a64f-6657dae1d4d5","order_by":1,"name":"Mohamed Chahine GHANEM","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABXUlEQVRIie3SP2vCQBgG8LccxOW1WU8i5hMUIoF0MPSLdFECyXJCJ3HoIAjponuK/RyhY0RIl0PXgB2UgpNDW0GQ0j932jYSsF0LzTMkDyE/7i5vAPLk+ZNBeSEAFBSoy063j+2y8vlCeXeLfiUuKvsvHCSQkhHCAXJy1R8+J+0aqIPuYja/tUE/F+WJTfCYOvHqom0jFEYzgvyLWHzsaIx7QO/jU6PBXaiKUr0Op6hQ1xkE3EVA1yCYfJOEGVrTH4FB6xZtiFIN6pZWlAS5SYq+3CEDgo97xHxpvknirQV5F8Rba6/hWJDJakfUZYZYWrMjCZOrRKCLoh2FESqFHtkRKldJN8a5VWOxhzRhLUEcFLZV6oeOIL5J5FkUujCGN+nx73rmlF3WKmrghaWNf1bRRaGbUJQueSDii1VU1ZnPlnHmF4B0CsbezOS8YDuvCH6I3smSPHny5Pnv+QD+j3tGR0MKUwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Liverpool","correspondingAuthor":true,"prefix":"","firstName":"Mohamed","middleName":"Chahine","lastName":"GHANEM","suffix":""},{"id":444214431,"identity":"47cb3bd7-f1dd-4e7e-8fb5-4648e6bc54dd","order_by":2,"name":"Karim OUAZZANE","email":"","orcid":"","institution":"London Metropolitan University","correspondingAuthor":false,"prefix":"","firstName":"Karim","middleName":"","lastName":"OUAZZANE","suffix":""},{"id":444214434,"identity":"2d8bdd1c-68f7-43ed-9441-a0fceffcc045","order_by":3,"name":"Hamza KHEDDAR","email":"","orcid":"","institution":"University of Medea","correspondingAuthor":false,"prefix":"","firstName":"Hamza","middleName":"","lastName":"KHEDDAR","suffix":""},{"id":444214436,"identity":"63302f34-ec57-4a94-a6f9-7b2ded909358","order_by":4,"name":"Marcio J. LACERDA","email":"","orcid":"","institution":"London Metropolitan University","correspondingAuthor":false,"prefix":"","firstName":"Marcio","middleName":"J.","lastName":"LACERDA","suffix":""}],"badges":[],"createdAt":"2025-04-12 16:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6435716/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6435716/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96865433,"identity":"4d657f90-e4c6-4626-a542-931d194a2fc3","added_by":"auto","created_at":"2025-11-27 01:23:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":814555,"visible":true,"origin":"","legend":"","description":"","filename":"ASystematicAnalysisontheuseofAITechniquesinIndustrialIoTDDoSAttacksDetectionandPrevention.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6435716/v1_covered_da879cd3-7ae6-4cae-b848-b685ae3cda90.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Systematic Analysis on the Use of AI Techniques in Industrial IoT DDoS Attacks Detection, Mitigation and Prevention","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":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":"Artificial Intelligence, IoT, IIoT, Critical National Infrastructure, Resilience, DDoS, Machine Learning, Deep Learning, Cyber security","lastPublishedDoi":"10.21203/rs.3.rs-6435716/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6435716/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Distributed Denial of Service (DDoS) attacks pose significant threats to Industrial Internet of Things (IIoT) environments, exacerbated by the resource constraints of IoT devices and the disruptive impact of such attacks. Conventional detection and prevention methods fall short of ensuring the availability and operational continuity required in industrial IIoT deployments. This article systematically analyses artificial intelligence (AI) techniques for detecting, preventing, and mitigating DDoS attacks in IIoT systems. We examine diverse AI-driven solutions, including machine learning (ML) and deep learning (DL) models, often integrated with traditional anomaly detection, signature-based systems, and blockchain technology. These hybrid approaches enhance real-time threat identification, adaptive defence mechanisms, and decentralized trust management, addressing the evolving sophistication of DDoS attacks. The study highlights AI’s potential to strengthen IIoT security and resilience, particularly in Critical National Infrastructures (CNIs), where uninterrupted operations are paramount. However, challenges such as computational overhead, model interpretability, and dataset scarcity in industrial settings remain critical barriers. Additionally, the dynamic IIoT topology and heterogeneous device ecosystems necessitate context-aware AI solutions. This analysis underscores the need for lightweight, explainable AI frameworks and collaborative defence strategies tailored to IIoT’s unique constraints. The paper identifies current research challenges and outlines future directions, emphasizing the integration of AI with emerging technologies like edge computing and federated learning to advance proactive, scalable DDoS defence mechanisms in industrial ecosystems ","manuscriptTitle":"A Systematic Analysis on the Use of AI Techniques in Industrial IoT DDoS Attacks Detection, Mitigation and Prevention","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-28 05:12:00","doi":"10.21203/rs.3.rs-6435716/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":"53de13c9-ac58-4db2-a746-fb2e709afe2c","owner":[],"postedDate":"April 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-27T01:23:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-28 05:12:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6435716","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6435716","identity":"rs-6435716","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}