Hybrid Cross-Temporal Contrastive Model with Spiking Energy-Efficient Network Intrusion Detection in IOMT

Hybrid Cross-Temporal Contrastive Model with Spiking Energy-Efficient Network Intrusion Detection in IOMT | 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 Hybrid Cross-Temporal Contrastive Model with Spiking Energy-Efficient Network Intrusion Detection in IOMT Fatma S. Alrayes, Mohammed Zakariah, Syed Umar Amin, Zafar Iqbal Khan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6767420/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract The Internet of Medical Things (IoMT), a key application of the Internet of Things (IoT), has played a key role, especially during the Covid 19 pandemic. Real-time patient monitoring and remote diagnostics help improve medical services, but this increases the mammoth size of network traffic, which impacts the security quite a bit. However, traditional intrusion detection systems lack synchronization between accuracy and energy efficiency in resource-constrained IoMT environments. To address this issue, we present a hybrid cross-temporal contrastive model coupled with a spiking energy-efficient network for intrusion detection. This approach uses contrastive learning to learn temporal dependencies in network traffic and spiking neural networks (SNNs) for energy-efficient computations. We evaluated the model on the WUSTL-EHMS-2020 dataset, which consists of 44 features (35 of them are network flow measurements, and 8 are biometric patient features), as well as the NSL-KDD dataset to perform a comparative validation. Furthermore, the experiment results prove that our proposed model achieves 99.95% accuracy on the WUSTL-EHMS-2020 dataset with an F1 score of 99.89%, precision of 98.23%, and recall of 99.55%, outperforming conventional models. The model attained 98.2% accuracy, 97.6% precision, 98.5% F1 score, and 97.3% recall on the NSL-KDD Dataset. Our approach shows that these results effectively secure IoMT networks at a low computational cost. Finally, the proposed hybrid model can achieve good performance and energy efficiency for intrusion detection in innovative healthcare systems. In future work, efforts will be made to improve the model's generalization property in diverse IoMT environments and minimize the energy consumption of spiking neural networks in real-time applications. Hybrid Cross Temporal Contrastive Model Spiking Neural Networks Energy-Efficient Network Intrusion Detection Internet of Medical Things (IoMT) Cybersecurity Machine Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 Jul, 2025 Reviews received at journal 29 Jul, 2025 Reviews received at journal 30 Jun, 2025 Reviewers agreed at journal 29 Jun, 2025 Reviewers agreed at journal 29 Jun, 2025 Reviewers agreed at journal 27 Jun, 2025 Reviewers agreed at journal 27 Jun, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviewers invited by journal 25 Jun, 2025 Editor assigned by journal 21 Jun, 2025 Submission checks completed at journal 08 Jun, 2025 First submitted to journal 28 May, 2025 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-6767420","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":477959960,"identity":"62b82a1d-5ee7-46e6-b580-46e3a17038e4","order_by":0,"name":"Fatma S. 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In future work, efforts will be made to improve the model's generalization property in diverse IoMT environments and minimize the energy consumption of spiking neural networks in real-time applications.\u003c/p\u003e","manuscriptTitle":"Hybrid Cross-Temporal Contrastive Model with Spiking Energy-Efficient Network Intrusion Detection in IOMT","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-30 09:35:46","doi":"10.21203/rs.3.rs-6767420/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-29T21:34:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-29T21:15:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-30T14:01:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280457339269119133163473617879554349065","date":"2025-06-29T13:11:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76532976068361694976833611698810167337","date":"2025-06-29T10:36:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257575081499750428086432797215074276289","date":"2025-06-27T08:32:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197512623469834290460625984552161183597","date":"2025-06-27T08:26:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201076432417277152214164038217101925771","date":"2025-06-25T12:10:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-25T08:18:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-21T15:55:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-09T03:42:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Computational Intelligence Systems","date":"2025-05-28T10:52:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-computational-intelligence-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [International Journal of Computational Intelligence Systems](https://link.springer.com/journal/44196)","snPcode":"44196","submissionUrl":"https://submission.springernature.com/new-submission/44196/3","title":"International Journal of Computational Intelligence Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bafa3792-7cda-44b8-b563-1940ffde9370","owner":[],"postedDate":"June 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-09T21:08:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-30 09:35:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6767420","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6767420","identity":"rs-6767420","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}