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A comparative malware analysis of XWorm and Nanocore: laying the groundwork for enhanced detection strategies

bracu.degree.levelUndergraduate
bracu.type.groupStudent Works
datacite.rightsOpen Access
dc.contributor.advisorHaque, S M Taiabul
dc.contributor.advisorAhmed, Md. Faisal
dc.contributor.authorShanto, Shakil Islam
dc.contributor.authorPunom, Afsana Mimi
dc.contributor.departmentDepartment of Computer Science and Engineering
dc.date.accessioned2025-03-24T05:05:08Z
dc.date.available2025-03-24T05:05:08Z
dc.date.copyright2024
dc.date.issued2024
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 46-48).
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024.en_US
dc.description.abstractMalware continues to evolve, posing a significant challenge to global cybersecurity through sophisticated techniques such as obfuscation, process injection, and persistent Command-and-Control (C2) communication. This study conducts a comparative analysis of two active malware strains, XWorm and NanoCore, to uncover shared tactics and unique features that enable their evasion and impact. By employing static, dynamic, and reverse engineering analyses, the research identifies commonalities in delivery methods, persistence mechanisms, and payload obfuscation. Tailored YARA rules are developed to enhance malware detection frameworks, providing practical tools for real-world applications. The findings emphasize the importance of behavior-driven detection strategies and propose generalized mechanisms to address broader malware families, moving beyond isolated strain analysis. This research not only bridges critical gaps in understanding modern malware but also lays a foundation for scalable and adaptive defense systems, contributing to the ongoing battle against evolving cyber threats.en_US
dc.description.degreeBachelor of Science in Computer Science and Engineering
dc.description.statementofresponsibilityShakil Islam Shanto
dc.description.statementofresponsibilityAfsana Mimi Punom
dc.format.extent48 pages
dc.identifier.otherID 24341292
dc.identifier.otherID 20101267
dc.identifier.urihttp://hdl.handle.net/10361/25771
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectXWormen_US
dc.subjectNanocoreen_US
dc.subjectDetection strategiesen_US
dc.subjectMalware analysisen_US
dc.subject.lcshComputer security.
dc.titleA comparative malware analysis of XWorm and Nanocore: laying the groundwork for enhanced detection strategiesen_US
dc.typeThesisen_US

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