Enhancing Obfuscated Malware Detection with Machine Learning Techniques

被引：7
作者：
Dang, Quang-Vinh [1 ]
机构：
[1] Ind Univ Ho Chi Minh City, Ho Chi Minh City, Vietnam
来源：
FUTURE DATA AND SECURITY ENGINEERING. BIG DATA, SECURITY AND PRIVACY, SMART CITY AND INDUSTRY 4.0 APPLICATIONS, FDSE 2022 | 2022年 / 1688卷
关键词：
Obfuscated malware; Malware detection; Machine learning;
D O I：
10.1007/978-981-19-8069-5_54
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：731 / 738
页数：8
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