A comparative malware analysis of XWorm and Nanocore: laying the groundwork for enhanced detection strategies
| bracu.degree.level | Undergraduate | |
| bracu.type.group | Student Works | |
| datacite.rights | Open Access | |
| dc.contributor.advisor | Haque, S M Taiabul | |
| dc.contributor.advisor | Ahmed, Md. Faisal | |
| dc.contributor.author | Shanto, Shakil Islam | |
| dc.contributor.author | Punom, Afsana Mimi | |
| dc.contributor.department | Department of Computer Science and Engineering | |
| dc.date.accessioned | 2025-03-24T05:05:08Z | |
| dc.date.available | 2025-03-24T05:05:08Z | |
| dc.date.copyright | 2024 | |
| dc.date.issued | 2024 | |
| dc.description | Cataloged from PDF version of thesis. | |
| dc.description | Includes bibliographical references (pages 46-48). | |
| dc.description | This 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.abstract | Malware 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.degree | Bachelor of Science in Computer Science and Engineering | |
| dc.description.statementofresponsibility | Shakil Islam Shanto | |
| dc.description.statementofresponsibility | Afsana Mimi Punom | |
| dc.format.extent | 48 pages | |
| dc.identifier.other | ID 24341292 | |
| dc.identifier.other | ID 20101267 | |
| dc.identifier.uri | http://hdl.handle.net/10361/25771 | |
| dc.language.iso | en | en_US |
| dc.publisher | BRAC University | en_US |
| dc.rights | BRAC 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.subject | XWorm | en_US |
| dc.subject | Nanocore | en_US |
| dc.subject | Detection strategies | en_US |
| dc.subject | Malware analysis | en_US |
| dc.subject.lcsh | Computer security. | |
| dc.title | A comparative malware analysis of XWorm and Nanocore: laying the groundwork for enhanced detection strategies | en_US |
| dc.type | Thesis | en_US |