Gorment, Nor Zakiah and Selamat, Ali and Krejcar, Ondrej (2022) Anti-obfuscation techniques: Recent analysis of malware detection. In: New Trends in Intelligent Software Methodologies, Tools and Techniques. Frontiers in Artificial Intelligence and Applications, 355 (NA). IOS Press BV, Amsterdam, Noord-Holland Netherlands, pp. 181-192. ISBN 978-164368316-4
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Official URL: http://dx.doi.org/10.3233/FAIA220249
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.
Item Type: | Book Section |
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Uncontrolled Keywords: | Anti-obfuscation, Comparative study, Machine learning algorithm, Malware detection, Obfuscation technique |
Subjects: | Q Science > QA Mathematics > QA76 Computer software |
Divisions: | Malaysia-Japan International Institute of Technology |
ID Code: | 100543 |
Deposited By: | Widya Wahid |
Deposited On: | 17 Apr 2023 06:52 |
Last Modified: | 17 Apr 2023 06:52 |
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