Predictive-CSM: Lightweight Fragment Security for 6LoWPAN IoT Networks

Predictive-CSM: Lightweight Fragment Security for 6LoWPAN IoT Networks | 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 Predictive-CSM: Lightweight Fragment Security for 6LoWPAN IoT Networks Somayeh Sobati Moghadam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6981184/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 Fragmentation is a routine part of communication in 6LoWPANbased IoT networks, needed to accommodate small frame sizes on constrained wireless links. But this process comes with an overlooked trade-off: individual fragments are typically stored and processed before their legitimacy is confirmed. For attackers, this offers a low-cost but powerful way to exhaust memory, jam communication, or confuse packet reassembly—all with just a few well-timed transmissions. In this work, we explore a defense strategy that takes a more adaptive, behavior-aware approach to this problem. Our system, called Predictive-CSM, introduces a combination of two lightweight mechanisms. The first tracks how each node behaves over time, rewarding consistent and successful interactions while quickly penalizing suspicious or failing patterns. The second checks the integrity of packet fragments using a chained hash, allowing incomplete or manipulated sequences to be caught early, before they can occupy memory or waste processing time. We put this system to the test using a set of targeted attack simulations, including early fragment injection, replayed headers, and flooding with fake data. Across all scenarios, Predictive-CSM preserved network delivery and maintained energy efficiency, even under pressure. Rather than relying on heavyweight cryptography or rigid filters, this approach allows constrained devices to adapt their defenses in real time—based on what they observe, not just what they’re told. In that way, it offers a step forward for securing fragmented communication in real-world IoT systems 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-6981184","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":496612496,"identity":"1645de3b-0167-417f-8939-d2adaf4502a6","order_by":0,"name":"Somayeh Sobati Moghadam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYJACZiDm4ZeA8tiI1GLAIzmDVC0MBjeIdZR8A/vDxwU1f2SMb7c/k2CosWPgkz6AX4vBAR5j4xnHDHjM7pwxk2A4lszAxpdAQAsDD5s0DxtQy40cNgkGtgMMbDyEHfZMmuefAY/xjHSgw/4RoYXhAIOZNG+bAY+BRIKZBGMbEVoMDgP9MrPPmEfiRo6xRWJfMg9hh7W3A0Psm5w9/4z0hzc+fLOTk+8h5DBmZE4CMBkQ0jAKRsEoGAWjgAgAANt5MOfC1qByAAAAAElFTkSuQmCC","orcid":"","institution":"Hakim Sabzevari University","correspondingAuthor":true,"prefix":"","firstName":"Somayeh","middleName":"Sobati","lastName":"Moghadam","suffix":""}],"badges":[],"createdAt":"2025-06-26 08:23:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6981184/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6981184/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104399718,"identity":"010ff096-3d80-4c7f-ad67-0d7946979764","added_by":"auto","created_at":"2026-03-11 12:07:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":353348,"visible":true,"origin":"","legend":"","description":"","filename":"JClusComp.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6981184/v1_covered_285305fe-748e-4c2d-ae90-99a7bc0a06fc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive-CSM: Lightweight Fragment Security for 6LoWPAN IoT Networks","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":"","lastPublishedDoi":"10.21203/rs.3.rs-6981184/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6981184/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Fragmentation is a routine part of communication in 6LoWPANbased IoT networks, needed to accommodate small frame sizes on constrained wireless links. But this process comes with an overlooked trade-off: individual fragments are typically stored and processed before their legitimacy is confirmed. For attackers, this offers a low-cost but powerful way to exhaust memory, jam communication, or confuse packet reassembly—all with just a few well-timed transmissions. In this work, we explore a defense strategy that takes a more adaptive, behavior-aware approach to this problem. Our system, called Predictive-CSM, introduces a combination of two lightweight mechanisms. The first tracks how each node behaves over time, rewarding consistent and successful interactions while quickly penalizing suspicious or failing patterns. The second checks the integrity of packet fragments using a chained hash, allowing incomplete or manipulated sequences to be caught early, before they can occupy memory or waste processing time. We put this system to the test using a set of targeted attack simulations, including early fragment injection, replayed headers, and flooding with fake data. Across all scenarios, Predictive-CSM preserved network delivery and maintained energy efficiency, even under pressure. Rather than relying on heavyweight cryptography or rigid filters, this approach allows constrained devices to adapt their defenses in real time—based on what they observe, not just what they’re told. In that way, it offers a step forward for securing fragmented communication in real-world IoT systems","manuscriptTitle":"Predictive-CSM: Lightweight Fragment Security for 6LoWPAN IoT Networks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-07 08:16:01","doi":"10.21203/rs.3.rs-6981184/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":"fd6f498e-1fdd-46a3-824d-80d5f19ab707","owner":[],"postedDate":"August 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-28T23:23:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-07 08:16:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6981184","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6981184","identity":"rs-6981184","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}