JOURNAL ARTICLE
Trojan Analysis using Malware Detection.
Published In: Grenze International Journal of Engineering & Technology (GIJET), 2025, v. 11, n. Part1. P. 599 1 of 3
Database: Applied Science & Technology Source Ultimate 2 of 3
Authored By: Turukumane, Anil Vittalrao; Reddy, CH. Rohith Sai Kumar; Vijjada, Charitha Sree; Baig, Saaduddin; Abhinav, B. Y. Sai 3 of 3
Abstract
Laying out possible defense strategies is helpful but it does little to help the defender in practical terms: weaim to bridge this gap with our initiative. Leveraging state-of-the-art tools such as Flare VM and REMnux, we give a detailed framework for ZeuS anatomy, behavior and spreading mechanism. This helps the cybersecurity community make an actionable defense plan against Zeus and its forms by finding important discoverable information, Indicators of Compromise (IOCs), along with retrospective IOCs. We then tailor our effort towards stopping and detracting from the emerging threat that banking Trojans such as Zeus have become, due to a shortage in effective analysis today by both researchers fighting them aroundevery corner of the internet more so than Security Practitioners defending against their endeavors. Our goal is to secure our defences through rigorous analysis and actionable knowledge against the dynamic threat landscape of contemporary malware. Execution of Zeus within a controlled environment, monitoring its behavior, and capturing run-time activities. Furthermore, we extend our analysis to incorporate threat intelligence from platforms like Virus Total and Hybrid Analysis, enriching our understanding of Zeus's characteristics, variants, and detection rates. In conclusion, this report provides a detailed abstract of our static and dynamic analysis of the Zeus trojan using Flare VM and REMNUX, offering a comprehensive overview of our methodologies, findings, and insights. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Grenze International Journal of Engineering & Technology (GIJET). 2025/01, Vol. 11, Issue Part1, p599
- Document Type:Article
- Subject Area:Computer Science
- Publication Date:2025
- ISSN:23955287
- Accession Number:186932674
- Copyright Statement:Copyright of Grenze International Journal of Engineering & Technology (GIJET) is the property of GRENZE Scientific Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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