SocialGuard: An Integrated Framework for Proactive Fake Account Detection Leveraging Behavioral APIs and Malicious URL Profiling

SocialGuard: An Integrated Framework for Proactive Fake Account Detection Leveraging Behavioral APIs and Malicious URL Profiling | 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 SocialGuard: An Integrated Framework for Proactive Fake Account Detection Leveraging Behavioral APIs and Malicious URL Profiling Nnaemeka Kingsley Ugwumba This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8085397/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 Social media platforms have become indispensable for communication, information dissemination, and business interactions; however, the proliferation of fake accounts threatens user trust, data integrity, and online safety. This study introduces SocialGuard, an integrated framework for proactive fake account detection that combines behavioral API monitoring with malicious URL profiling. The framework extracts hybrid features from user activity, interaction frequency, and external URL patterns, utilizing BERT embeddings for textual behavior and XGBoost for classification. Experiments conducted on a benchmark Kaggle dataset achieved an overall accuracy of 99.51%, outperforming baseline BERT-only models. The results demonstrate that incorporating behavioral dynamics and malicious link profiling significantly enhances the detection of deceptive and automated accounts. This work contributes a scalable, data-driven architecture suitable for real-time deployment in modern social ecosystems. Artificial Intelligence and Machine Learning Fake account detection Behavioral analysis Malicious URL profiling BERT embeddings XGBoost Hybrid model Social media security Ensemble learning Deep learning Bot detection Cyber threat intelligence SocialGuard framework Full Text Additional Declarations The authors declare no competing interests. Below are the dataset citations for our study: Vibodh Bhosure. (2023). Twitter Fake Profile Dataset [Dataset]. Kaggle. Retrieved from https://www.kaggle.com/datasets/vibodhbhosure/twitter-fake-profile-dataset bitandatom. (2022). Social Network Fake Account Dataset [Dataset]. Kaggle. Retrieved from https://www.kaggle.com/datasets/bitandatom/social-network-fake-account-dataset sid321axn. (2021). Malicious URLs Dataset [Dataset]. Kaggle. Retrieved from https://www.kaggle.com/datasets/sid321axn/malicious-urls-dataset Supplementary Files featureanalysisreport.png Feature Analysis Report projectsummary.txt Project Summary 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-8085397","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":543235451,"identity":"6d50ca9e-67d9-4b12-ba4f-b4341890491d","order_by":0,"name":"Nnaemeka Kingsley 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