Self-Attention based Traffic Anomaly Detection for Cloud Servers using Line Chart Patch Filling

Self-Attention based Traffic Anomaly Detection for Cloud Servers using Line Chart Patch Filling | 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 Self-Attention based Traffic Anomaly Detection for Cloud Servers using Line Chart Patch Filling Qingyu Gao, Zhenguo Gao, Yiqin Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8153838/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 With the rapid expansion of cloud servers in applications, detecting anomalies in network traffic accessing cloud servers has become a core concern of cloud server providers. Traditional anomaly detection methods suffer from low accuracy and inefficiency, thus failing to meet the demands. Meanwhile, deep learning techniques struggle with the issues of data imbalance. To address these challenges, we propose Line Chart Patch Filling assisted Self Attention (LCPF-SA) mode. LCPF-SA first transforms raw time series data into images by producing line chart, filling parts of the line chart with selected patterns. The images are then classified by using a multi-layer model that integrates Convolutional Neural Network (CNN) and Self-Attention (SA) mechanism. To facilitate the training and evaluation of network traffic anomaly detection algorithms, we construct a dedicated dataset and named HQUNetTraffic dataset. The performance of LCPF-SA is comparatively evaluated on our dataset and a public ElectricDevices dataset from UCR. Experiment results show that LCPF-SA outperforms existing anomaly detection techniques and exhibits greater robustness to data imbalance. Anomaly Detection Network Traffic Convolutional Neural Network Self-Attention Mechanism Line Chart Patch Filling 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-8153838","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":565522462,"identity":"7fc731d2-bea1-4aac-aae8-cd2a17c84804","order_by":0,"name":"Qingyu Gao","email":"","orcid":"","institution":"HuaQiao University","correspondingAuthor":false,"prefix":"","firstName":"Qingyu","middleName":"","lastName":"Gao","suffix":""},{"id":565522463,"identity":"3362bf82-4c7a-49c0-91bc-f2142d615d1d","order_by":1,"name":"Zhenguo 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