Abstract
Obfuscated malware poses a challenge to traditional malware detection methods as it uses various techniques to disguise its behavior and evade detection. This paper focuses on the impacts of obfuscated malware detection techniques using a variety of detection methods. Furthermore, this paper discusses the current state of obfuscated malware, the methods used to detect it, and the limitations of those methods. The impact of obfuscation on the effectiveness of detection methods is also discussed. An approach for the creation of advanced detection techniques based on machine learning algorithms is offered, along with an empirical examination of malware detection performance assessment to battle obfuscated malware. Overall, this paper highlights the importance of staying ahead of the constantly evolving threat landscape to safeguard computer networks and systems.
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Acknowledgements
This work was supported in part by the Ministry of Higher Education through the Fundamental Research Grant Scheme under Grant FRGS/1/2018/ICT04/UTM/01/1; and in part by the Faculty of Informatics and Management, University of Hradec Králové, through the Specific Research Project (SPEV), “Smart Solutions in Ubiquitous Computing Environments”, under Grant 2102/2023. We are also grateful for the support of student Michal Dobrovolny in consultations regarding application aspects.
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Gorment, N.Z., Selamat, A., Krejcar, O. (2023). Obfuscated Malware Detection: Impacts on Detection Methods. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2023. Communications in Computer and Information Science, vol 1863. Springer, Cham. https://doi.org/10.1007/978-3-031-42430-4_5
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