{"paper_id":"3f7a7f00-7dda-44cd-bc8a-b73c9efecbe5","body_text":"Decoding Cyber Threats: Advanced Techniques In Malware Detection, Analysis, and URL 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 Decoding Cyber Threats: Advanced Techniques In Malware Detection, Analysis, and URL Detection Dr. Geeta Padole, Ruthvik Reddy Annareddy, Avinash Mothukuri, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4324837/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 The digital security landscape keeps on changing, with malware posing an extraordinary and growing threat across various cyber domains. Malware or malicious software is getting more complicated as it uses different techniques to enter systems, exploit their weaknesses and compromise the integrity of data in them. The increasing number of malware attacks can be attributed to the rapid developments in technology and the increased connectivity through internet that gives better avenues for attackers. To mitigate these threats, this paper presents a project that combines advanced machine learning techniques aimed at generating powerful analysis tools for both URL and malware detection thereby distinguishing itself from traditional approaches depending on signature-based detection and heuristic methods only. XGBoost and LightGBM algorithms are used in URL analysis to classify URLs based on their structural properties or patterns which distinguish benign URLs from malicious ones. Therefore, this enables early warning systems before users click on any web link. On the other hand, malware analysis is focused on examining executable files to determine whether they are safe or not. It incorporates a wide range of machine learning algorithms such as Random Forest, KNN, Logistic Regression and Gradient Boosting beyond conventional signature-based antivirus methods. This approach allows for the dynamic learning from new malware behaviors, improving detection rates and adapting to evolving threats. URL detection Machine learning Cybersecurity Sophisticated threats Proactive detection and digital security Full Text Additional Declarations The authors declare no competing interests. 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. 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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-4324837\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":295476187,\"identity\":\"88b16c1e-9344-48d3-9d33-eefb84abe9ff\",\"order_by\":0,\"name\":\"Dr. Geeta Padole\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"Dr.\",\"firstName\":\"Geeta\",\"middleName\":\"\",\"lastName\":\"Padole\",\"suffix\":\"\"},{\"id\":295476188,\"identity\":\"b3050b06-73ad-4399-83db-f324187e18a6\",\"order_by\":1,\"name\":\"Ruthvik Reddy 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