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Obfuscated Malware Detection: Impacts on Detection Methods

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...

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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
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id my.uniten.dspace-34469
record_format dspace
spelling my.uniten.dspace-344692024-10-14T11:20:00Z Obfuscated Malware Detection: Impacts on Detection Methods Gorment N.Z. Selamat A. Krejcar O. 57201987388 24468984100 14719632500 Machine leaning algorithm Malware detection Obfuscated malware Learning algorithms Malware 'current Advanced detections Detection methods Effectiveness of detection methods Machine leaning Machine leaning algorithm Malware detection Malwares Obfuscated malware On-machines Machine learning 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. Final 2024-10-14T03:20:00Z 2024-10-14T03:20:00Z 2023 Conference Paper 10.1007/978-3-031-42430-4_5 2-s2.0-85174520622 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174520622&doi=10.1007%2f978-3-031-42430-4_5&partnerID=40&md5=1e32964a2c9b0db3f8a96fed253f8537 https://irepository.uniten.edu.my/handle/123456789/34469 1863 CCIS 55 66 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Machine leaning algorithm
Malware detection
Obfuscated malware
Learning algorithms
Malware
'current
Advanced detections
Detection methods
Effectiveness of detection methods
Machine leaning
Machine leaning algorithm
Malware detection
Malwares
Obfuscated malware
On-machines
Machine learning
spellingShingle Machine leaning algorithm
Malware detection
Obfuscated malware
Learning algorithms
Malware
'current
Advanced detections
Detection methods
Effectiveness of detection methods
Machine leaning
Machine leaning algorithm
Malware detection
Malwares
Obfuscated malware
On-machines
Machine learning
Gorment N.Z.
Selamat A.
Krejcar O.
Obfuscated Malware Detection: Impacts on Detection Methods
description 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.
author2 57201987388
author_facet 57201987388
Gorment N.Z.
Selamat A.
Krejcar O.
format Conference Paper
author Gorment N.Z.
Selamat A.
Krejcar O.
author_sort Gorment N.Z.
title Obfuscated Malware Detection: Impacts on Detection Methods
title_short Obfuscated Malware Detection: Impacts on Detection Methods
title_full Obfuscated Malware Detection: Impacts on Detection Methods
title_fullStr Obfuscated Malware Detection: Impacts on Detection Methods
title_full_unstemmed Obfuscated Malware Detection: Impacts on Detection Methods
title_sort obfuscated malware detection: impacts on detection methods
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2024
_version_ 1814061181906714624
score 13.214268

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