Abstract
Obfuscated malware is malware that tries to be hidden from malware detection software. While there are some advances in the malware detection research community in recent years, modern malware uses multiple techniques to avoid being detected by the anti-malware system. In this research, we aim to improve the detection quality of malware by using state-of-the-art machine learning algorithms. The experimental results show that our proposed methods outperform state-of-the-art research studies.
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Dang, QV. (2022). Enhancing Obfuscated Malware Detection with Machine Learning Techniques. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2022. Communications in Computer and Information Science, vol 1688. Springer, Singapore. https://doi.org/10.1007/978-981-19-8069-5_54
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