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 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. � 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.