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A review of polymorphic malware detection techniques

Alrzini, Joma Rajab Salim; Pennington, Diane


Joma Rajab Salim Alrzini


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.


Alrzini, J. R. S., & Pennington, D. (2020). A review of polymorphic malware detection techniques. International Journal of Advanced Research in Engineering and Technology, 11(12), 1238-1247.

Journal Article Type Article
Publication Date 2020-12
Deposit Date Feb 3, 2023
Journal International Journal of Advanced Research in Engineering and Technology
Print ISSN 0976-6480
Peer Reviewed Peer Reviewed
Volume 11
Issue 12
Pages 1238-1247
Keywords anti-detection; polymorphic automated testing; abnormal threats; packed malicious file
Publisher URL