Obfuscated Malware Detection: Impacts on Detection Methods

• Main Authors: Gorment N.Z., Selamat A., Krejcar O.
• Other Authors: 57201987388
• Format: Conference Paper
• Published: Springer Science and Business Media Deutschland GmbH 2024
• Subjects: Machine leaning algorithm; Malware detection; Obfuscated malware; Learning algorithms; Malware; 'current; Advanced detections; Detection methods; Effectiveness of detection methods; Machine leaning; Malwares; On-machines; Machine learning
• Institution: Universiti Tenaga Nasional

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 discuss...