Publication: Anti-Obfuscation Techniques: Recent Analysis of Malware Detection
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Date
2022
Authors
Gorment N.Z.
Selamat A.
Krejcar O.
Journal Title
Journal ISSN
Volume Title
Publisher
IOS Press BV
Abstract
One of the challenging issues in detecting the malware is that modern stealthy malware prefers to stay hidden during their attacks on our devices and be obfuscated. They can evade antivirus scanners or other malware analysis tools and might attempt to thwart modern detection, including altering the file attributes or performing the action under the pretense of authorized services. Therefore, it's crucial to understand and analyze how malware implements obfuscation techniques to curb these concerns. This paper is dedicated to presenting an analysis of anti-obfuscation techniques for malware detection. Furthermore, an empirical analysis of the performance evaluation of malware detection using machine learning algorithms and the obfuscation techniques was conducted to address the associated issues that might help researchers plan and generate an efficient algorithm for malware detection. � 2022 The authors and IOS Press. All rights reserved.
Description
Machine learning; Malware; Analysis tools; Anti virus; Anti-obfuscation; Comparatives studies; Machine learning algorithms; Malware analysis; Malware detection; Malwares; Obfuscation technique; Stealthy malware; Learning algorithms