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Malware authorship attribution: Unmasking the culprits behind malicious software
- 1 Capitol Technology University
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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
[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.
Cite this article
Chia,H.L.B. (2023). Malware authorship attribution: Unmasking the culprits behind malicious software. Advances in Engineering Innovation,2,45-49.
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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Journal：Advances in Engineering Innovation
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