Doctorate Division, Capitol Technology University, United States of America.
Review Article
World Journal of Advanced Research and Reviews, 2024, 23(03), 1313–1323
Received on 31 July 2024; revised on 08 September 2024; accepted on 10 September 2024
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.
Malware Attribution; Static Analysis; Dynamic Analysis; Machine Learning; Malware Obfuscation; Cybersecurity
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Luay Bahjat Albtosh. Malware authorship attribution: Unmasking the culprits behind malicious software. World Journal of Advanced Research and Reviews, 2024, 23(3), 1313-1323. Article DOI: https://doi.org/10.30574/wjarr.2024.23.3.2769