Polymorphic Malware in Executable Files and the Approaches towards their Detection and Extraction
|Faiz Baothman1 , Muzammil H Mohammed2|
1 Dept. of Computer Science, College of Computers and Information Technology (Taif University), Taif, Saudi Arabia.
2 Dept. of Information Technology, College of Computers and Information Technology, (Taif University), Taif, Saudi Arabia.
|Correspondence should be addressed to: firstname.lastname@example.org .|
Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-2 , Page no. 12-17, Feb-2018
Online published on Feb 28, 2018
Copyright © Faiz Baothman, Muzammil H Mohammed . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: Faiz Baothman, Muzammil H Mohammed, “Polymorphic Malware in Executable Files and the Approaches towards their Detection and Extraction”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.12-17, 2018.
MLA Style Citation: Faiz Baothman, Muzammil H Mohammed "Polymorphic Malware in Executable Files and the Approaches towards their Detection and Extraction." International Journal of Computer Sciences and Engineering 6.2 (2018): 12-17.
APA Style Citation: Faiz Baothman, Muzammil H Mohammed, (2018). Polymorphic Malware in Executable Files and the Approaches towards their Detection and Extraction. International Journal of Computer Sciences and Engineering, 6(2), 12-17.
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|The malwares which are present with subtle with polymorphic techniques like self-mutation and emulation based mostly analysis evasion. Most anti-malware techniques are engulfed by the polymorphic malware threats that self-mutate with completely different variants at each attack. This analysis aims to contribute to the detection of malicious codes, particularly polymorphic malware by utilizing advanced static and advanced dynamic analysis for extraction of a lot of informative key options of a malware through code analysis, memory analysis and activity analysis. Correlation based mostly feature choice rules are rework features; i.e. filtering and choosing best and relevant options. A machine learning technique known as K-Nearest Neighbor (K-NN) are used for classification and detection of polymorphic malware analysis, results are supported the subsequent measuring metrics— True Positive Rate (TPR), False Positive Rate (FPR) and therefore the overall detection accuracy of experiments.|
|Key-Words / Index Term :|
|Malware Detection, Static Analysis, Dynamic Analysis, Polymorphic Malware, Machine Learning|
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