{"paper_id":"24f61e5a-144b-4ea5-b384-7fa346c7625c","body_text":"A novel Federated Learning method with Domain Adaptation and Model Selection for Intrusion Detection | 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 novel Federated Learning method with Domain Adaptation and Model Selection for Intrusion Detection Xiaoyu Li, Kai Su, Jingjing Liu, Weifei Wu, Liying Bao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4661580/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 At present, the intrusion detection data of a single organization is insufficient, the intrusion detection data of each organization cannot meet the conditions of independent and identical distribution, and the distribution of each organization in different geographical locations will also cause the transmission difficulties of data and data leakage problems, which poses a huge challenge to the existing intrusion detection methods based on machine learning. To remedy the problem, a novel Federated Learning Algorithm with Domain Adaptation and Model Selection for Intrusion Detection (FEDTLDAM) is proposed in this paper. FEDTLDAM uses the proposed transfer deep learning model under the federation learning framework to train the local learning model on each organization's local intrusion detection data (source domain) and the global model of the public server (target), and t the designed local model selection method was used to select the local model. Only the local model parameters that meet the conditions are uploaded to the public cloud server to share the knowledge of each organization model, improve the intrusion detection effect of the target model, and ensure the security and privacy of the data of each organization. The domain adaptation strategy of the transfer deep learning model not only considers the difference of distribution between marginal probability and conditional probability, but also utilized the designed weighted method to measure the importance of the above two distribution differences to improve the model learning effect. The model selection method reduces the influence of bad local models, reduces the communication overhead, and improves the global model detection performance. The proposed algorithm FEDTLDAM is verified by experiments on three intrusion detection datasets ISCX2012, NSL-KDD and CICIDS2017, and the results show that compared with the benchmark algorithms, the proposed method has significantly improved the detection accuracy, training efficiency and other key performance indicators. In addition, FEDTLDAM also has good ability of generalization and data privacy protection, and significant application potential in the field of network security. Federated Learning Domain Adaptation Convolutional Neural Networks Intrusion Detection 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-4661580\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":329430219,\"identity\":\"f5e71dfa-cecf-434e-b696-2502882298af\",\"order_by\":0,\"name\":\"Xiaoyu Li\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYFACHjYGhgqben5m5sMPSNByJi1Bsp0tzYB4LYxthxMMzvMoSBClQX5G7rGHP9uY84wP8zAYMNTYRBPUYnAjL91A4hxbsdlh3gMPGI6l5TYQ1CKdYyZhUMbDuO0wX4IBY8NhwlrkZwO1JLBJMG5u5jGQIEoLw22glgNtBokbmInVYnD/Xbphw5kEY4nDwEBOIMYv8j1njz38UfFfjr//8OEHH2psiHAYCkggTfkoGAWjYBSMAlwAAHgtPmNTh23uAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"Beijing Institute of Remote Sensing Equipment\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Xiaoyu\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"},{\"id\":329430220,\"identity\":\"6a8b5b58-be8a-4ef7-9af0-4df8496c2061\",\"order_by\":1,\"name\":\"Kai Su\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Academy of China Chang Feng Electro-Mechanical Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Kai\",\"middleName\":\"\",\"lastName\":\"Su\",\"suffix\":\"\"},{\"id\":329430221,\"identity\":\"ac4c8694-62df-4b46-ab18-6fbc051eac05\",\"order_by\":2,\"name\":\"Jingjing Liu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Beijing Institute of Remote Sensing Equipment\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jingjing\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"},{\"id\":329430222,\"identity\":\"03643660-cf21-4553-9aeb-f19a395e1195\",\"order_by\":3,\"name\":\"Weifei Wu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Beijing Institute of Remote Sensing Equipment\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Weifei\",\"middleName\":\"\",\"lastName\":\"Wu\",\"suffix\":\"\"},{\"id\":329430223,\"identity\":\"b8758374-3a48-4c69-9d8c-1a736bb3a22e\",\"order_by\":4,\"name\":\"Liying Bao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Beijing Institute of Remote Sensing Equipment\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Liying\",\"middleName\":\"\",\"lastName\":\"Bao\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-06-30 07:08:22\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4661580/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4661580/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":62254685,\"identity\":\"e5857ded-bb32-4e26-861f-0e12ad202c97\",\"added_by\":\"auto\",\"created_at\":\"2024-08-12 07:04:47\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":481442,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"AnovelFederatedLearningmethodwithDomainAdaptationandModelSelectionforIntrusionDetection.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4661580/v1_covered_0c02484b-18c7-4b6d-8e28-313368a01c78.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"A novel Federated Learning method with Domain Adaptation and Model Selection for Intrusion Detection\",\"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\":\"info@researchsquare.com\",\"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\":\"Federated Learning, Domain Adaptation, Convolutional Neural Networks, Intrusion Detection\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4661580/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4661580/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eAt present, the intrusion detection data of a single organization is insufficient, the intrusion detection data of each organization cannot meet the conditions of independent and identical distribution, and the distribution of each organization in different geographical locations will also cause the transmission difficulties of data and data leakage problems, which poses a huge challenge to the existing intrusion detection methods based on machine learning. To remedy the problem, a novel Federated Learning Algorithm with Domain Adaptation and Model Selection for Intrusion Detection (FEDTLDAM) is proposed in this paper. FEDTLDAM uses the proposed transfer deep learning model under the federation learning framework to train the local learning model on each organization's local intrusion detection data (source domain) and the global model of the public server (target), and t the designed local model selection method was used to select the local model. Only the local model parameters that meet the conditions are uploaded to the public cloud server to share the knowledge of each organization model, improve the intrusion detection effect of the target model, and ensure the security and privacy of the data of each organization. The domain adaptation strategy of the transfer deep learning model not only considers the difference of distribution between marginal probability and conditional probability, but also utilized the designed weighted method to measure the importance of the above two distribution differences to improve the model learning effect. The model selection method reduces the influence of bad local models, reduces the communication overhead, and improves the global model detection performance. The proposed algorithm FEDTLDAM is verified by experiments on three intrusion detection datasets ISCX2012, NSL-KDD and CICIDS2017, and the results show that compared with the benchmark algorithms, the proposed method has significantly improved the detection accuracy, training efficiency and other key performance indicators. In addition, FEDTLDAM also has good ability of generalization and data privacy protection, and significant application potential in the field of network security.\\u003c/p\\u003e\",\"manuscriptTitle\":\"A novel Federated Learning method with Domain Adaptation and Model Selection for Intrusion Detection\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-07-24 06:53:30\",\"doi\":\"10.21203/rs.3.rs-4661580/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"1f16e6e6-b76b-4173-915b-23a2d6c0bd2e\",\"owner\":[],\"postedDate\":\"July 24th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-08-12T06:56:40+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-07-24 06:53:30\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4661580\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4661580\",\"identity\":\"rs-4661580\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}