Attack Detection Model for BCoT based on Contrastive Variational Autoencoder and Metric Learning

Attack Detection Model for BCoT based on Contrastive Variational Autoencoder and Metric Learning | 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 Attack Detection Model for BCoT based on Contrastive Variational Autoencoder and Metric Learning Chunwang Wu, Xiaolei Liu, Kangyi Ding, Bangzhou Xin, Jiazhong Lu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4126223/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Aug, 2024 Read the published version in Journal of Cloud Computing → Version 1 posted 7 You are reading this latest preprint version Abstract With development of blockchain technology, clouding computing and Internet of Things (IoT), blockchain and cloud of things (BCoT) has become development tendency. But the security has become the most development hinder of BCoT. Attack detection model is a crucial part of attack revelation mechanism for BCoT. As a consequence, attack detection model has received more concerned. Due to the great diversity and variation of network attacks aiming to BCoT, tradition attack detection models are not suitable for BCoT. In this paper, we propose a novel attack detection model for BCoT, denoted as cVAE-DML. The novel model is based on contrastive variational autoencoder (cVAE) and deep metric learning (DML). By training the cVAE, the proposed model generates private features for attack visiting information as well as shared features between attack visiting information and normal visiting information. Based on those generated features, the proposed model can generate representative new samples to balance the training dataset. At last, the decoder of cVAE is connected to the deep metric learning network to detect attack aiming to BCoT. The efficiency of cVAE-DML is verified using the CIC-IDS 2017 dataset and CSE-CIC-IDS 2018 dataset. The results show that cVAE-DML can improve attack detection efficiency even under the condition of unbalanced samples. attack detection BCoT metric learning contrastive variational autoencoder oversample Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Aug, 2024 Read the published version in Journal of Cloud Computing → Version 1 posted Editorial decision: Revision requested 03 Apr, 2024 Reviews received at journal 03 Apr, 2024 Reviewers agreed at journal 22 Mar, 2024 Reviewers invited by journal 21 Mar, 2024 Editor assigned by journal 19 Mar, 2024 Submission checks completed at journal 19 Mar, 2024 First submitted to journal 18 Mar, 2024 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-4126223","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":281196375,"identity":"d647abe9-ccc5-4a2b-bb76-7f742e481ee9","order_by":0,"name":"Chunwang Wu","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Chunwang","middleName":"","lastName":"Wu","suffix":""},{"id":281196376,"identity":"eaf39590-fad2-4cba-87b5-0c2197474dfd","order_by":1,"name":"Xiaolei Liu","email":"","orcid":"","institution":"China Academy of Engineering 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