Anti-Obfuscation Techniques: Recent Analysis of Malware Detection
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
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IOS Press BV
2023
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my.uniten.dspace-267482023-05-29T17:36:30Z Anti-Obfuscation Techniques: Recent Analysis of Malware Detection Gorment N.Z. Selamat A. Krejcar O. 57201987388 24468984100 14719632500 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 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. Final 2023-05-29T09:36:30Z 2023-05-29T09:36:30Z 2022 Conference Paper 10.3233/FAIA220249 2-s2.0-85139749407 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139749407&doi=10.3233%2fFAIA220249&partnerID=40&md5=470a2c46a06666b19d7c5cb453892c6c https://irepository.uniten.edu.my/handle/123456789/26748 355 181 192 IOS Press BV Scopus |
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| 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 |
| author2 |
57201987388 |
| author_facet |
57201987388 Gorment N.Z. Selamat A. Krejcar O. |
| format |
Conference Paper |
| author |
Gorment N.Z. Selamat A. Krejcar O. |
| spellingShingle |
Gorment N.Z. Selamat A. Krejcar O. Anti-Obfuscation Techniques: Recent Analysis of Malware Detection |
| author_sort |
Gorment N.Z. |
| title |
Anti-Obfuscation Techniques: Recent Analysis of Malware Detection |
| title_short |
Anti-Obfuscation Techniques: Recent Analysis of Malware Detection |
| title_full |
Anti-Obfuscation Techniques: Recent Analysis of Malware Detection |
| title_fullStr |
Anti-Obfuscation Techniques: Recent Analysis of Malware Detection |
| title_full_unstemmed |
Anti-Obfuscation Techniques: Recent Analysis of Malware Detection |
| title_sort |
anti-obfuscation techniques: recent analysis of malware detection |
| publisher |
IOS Press BV |
| publishDate |
2023 |
| _version_ |
1806428132601757696 |
| score |
13.214268 |