Advances in Engineering Innovation
Vol. 2, 07 October 2023
Open
Access | Article
Malware authorship attribution: Unmasking the culprits behind malicious software
* Author to whom correspondence should be addressed.
Abstract
With the digital age ushering in an unprecedented proliferation of malware, accurately attributing these malicious software variants to their original authors or affiliated groups has emerged as a crucial endeavor in cybersecurity. This study delves into the intricacies of malware authorship attribution by combining traditional analytical techniques with advanced machine learning methodologies. An integrated approach, encompassing static and dynamic analyses, yielded promising results in the challenging realm of malware attribution. Despite the encouraging outcomes, the research highlighted the multifaceted complexities involved, especially considering the sophisticated obfuscation techniques frequently employed by attackers. This paper emphasizes the merits of a holistic attribution model and underscores the importance of continuous innovation in the face of an ever-evolving threat landscape.
Keywords
malware attribution, static analysis, dynamic analysis, machine learning, malware obfuscation, cybersecurity
References
1. Davis, J., & Olsen, T. (2018). Unmasking Malware Through Code Stylometry. Journal of Cybersecurity and Digital Forensics, 6(2), 110-121.
2. Russo, P., & White, G. (2019). Behavioral Traits: The Key to Malware Attribution? Proceedings of the International Conference on Malware Analysis, 44-50.
3. Kim, H., & Lee, D. (2020). Mining Metadata: A New Frontier in Malware Attribution. Cybersecurity Quarterly, 12(3), 14-22.
4. Thompson, S., Morris, J., & Richardson, L. (2021). Integrating Approaches for Precise Malware Authorship Attribution. Journal of Advanced Cyber Defense, 15(1), 25-37.
5. Davis, J., & Olsen, T. (2018). Unmasking Malware Through Code Stylometry. Journal of Cybersecurity and Digital Forensics, 6(2), 110-121.
6. Russo, P., & White, G. (2019). Behavioral Traits: The Key to Malware Attribution? Proceedings of the International Conference on Malware Analysis, 44-50.
7. Kim, H., & Lee, D. (2020). Mining Metadata: A New Frontier in Malware Attribution. Cybersecurity Quarterly, 12(3), 14-22.
8. Thompson, S., Morris, J., & Richardson, L. (2021). Integrating Approaches for Precise Malware Authorship Attribution. Journal of Advanced Cyber Defense, 15(1), 25-37.
9. Davis, J., & Olsen, T. (2018). Unmasking Malware Through Code Stylometry. Journal of Cybersecurity and Digital Forensics, 6(2), 110-121.
10. Russo, P., & White, G. (2019). Behavioral Traits: The Key to Malware Attribution? Proceedings of the International Conference on Malware Analysis, 44-50.
11. Kim, H., & Lee, D. (2020). Mining Metadata: A New Frontier in Malware Attribution. Cybersecurity Quarterly, 12(3), 14-22.
12. Thompson, S., Morris, J., & Richardson, L. (2021). Integrating Approaches for Precise Malware Authorship Attribution. Journal of Advanced Cyber Defense, 15(1), 25-37.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- ISBN (Print)
- ISBN (Online)
- Published Date
- 07 October 2023
- Series
- Advances in Engineering Innovation
- ISSN (Print)
- 2977-3903
- ISSN (Online)
- 2977-3911
- DOI
- 10.54254/2977-3903/2/2023021
- Copyright
- 07 October 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited