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- W4224452014 abstract "Malware has grown in popularity as a method of conducting cyber assaults in former decades as a result of numerous new deception methods employed by malware. To preserve networks, information, and intelligence, malware must be detected as soon as feasible. This article compares various attribute extraction techniques with distinct machine learning algorithms for static malware classification and detection. The findings indicated that merging PCA attribute extraction and SVM classifier results in the highest correct rate with the fewest possible attributes, and this paper discusses sophisticated malware, their detection techniques, and how and where to defend systems and data from malware attacks. Overall, 96% the proposed method determines the malware more accurately than the existing methods." @default.
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- W4224452014 date "2022-04-25" @default.
- W4224452014 modified "2023-10-14" @default.
- W4224452014 title "An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques" @default.
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- W4224452014 doi "https://doi.org/10.1155/2022/5061059" @default.
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