A review of polymorphic malware detection techniques
Alrzini, Joma Rajab Salim and Pennington, Diane (2020) A review of polymorphic malware detection techniques. International Journal of Advanced Research in Engineering and Technology, 11 (12). pp. 1238-1247. ISSN 0976-6499 (https://doi.org/10.34218/IJARET.11.12.2020.119)
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Abstract
Despite the continuous updating of anti-detection systems for malicious programs (malware), malware has moved to an abnormal threat level; it is being generated and spread faster than before. One of the most serious challenges faced by anti-detection malware programs is an automatic mutation in the code; this is called polymorphic malware via the polymorphic engine. In this case, it is difficult to block the impact of signature-based detection. Hence new techniques have to be used in order to analyse modern malware. One of these techniques is machine learning algorithms in a virtual machine (VM) that can run the packed malicious file and analyse it dynamically through automated testing of the code. Moreover, recent research used image processing techniques with deep learning framework as a hybrid method with two analysis types and extracting a feature engineering approach in the analysis process in order to detect polymorphic malware efficiently. This paper presents a brief review of the latest applied techniques against this type of malware with more focus on the machine learning method for analysing and detecting polymorphic malware. It will discuss briefly the merits and demerits of it.
ORCID iDs
Alrzini, Joma Rajab Salim and Pennington, Diane ORCID: https://orcid.org/0000-0003-1275-7054;-
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Item type: Article ID code: 75235 Dates: DateEvent28 December 2020Published15 December 2020AcceptedSubjects: Science > Mathematics > Electronic computers. Computer science Department: Faculty of Science > Computer and Information Sciences Depositing user: Pure Administrator Date deposited: 02 Feb 2021 09:57 Last modified: 28 Nov 2024 01:21 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/75235