Publication:
Obfuscated Malware Detection: Impacts on Detection Methods

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dc.citedby2
dc.contributor.authorGorment N.Z.en_US
dc.contributor.authorSelamat A.en_US
dc.contributor.authorKrejcar O.en_US
dc.contributor.authorid57201987388en_US
dc.contributor.authorid24468984100en_US
dc.contributor.authorid14719632500en_US
dc.date.accessioned2024-10-14T03:20:00Z
dc.date.available2024-10-14T03:20:00Z
dc.date.issued2023
dc.description.abstractObfuscated 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-031-42430-4_5
dc.identifier.epage66
dc.identifier.scopus2-s2.0-85174520622
dc.identifier.spage55
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85174520622&doi=10.1007%2f978-3-031-42430-4_5&partnerID=40&md5=1e32964a2c9b0db3f8a96fed253f8537
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34469
dc.identifier.volume1863 CCIS
dc.pagecount11
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleCommunications in Computer and Information Science
dc.subjectMachine leaning algorithm
dc.subjectMalware detection
dc.subjectObfuscated malware
dc.subjectLearning algorithms
dc.subjectMalware
dc.subject'current
dc.subjectAdvanced detections
dc.subjectDetection methods
dc.subjectEffectiveness of detection methods
dc.subjectMachine leaning
dc.subjectMachine leaning algorithm
dc.subjectMalware detection
dc.subjectMalwares
dc.subjectObfuscated malware
dc.subjectOn-machines
dc.subjectMachine learning
dc.titleObfuscated Malware Detection: Impacts on Detection Methodsen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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