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- W3203077462 abstract "Any malicious code or program that affects the system or the software program is, in general, described as malware. This malware is usually harmful if not found on the right note. With evolving technologies, this malware tends to grow in different forms as they are created to steal information or make any potential hazard to the user. Machine learning comes on the line when detecting malware with various machine algorithms like random forest, K-nearest neighbor, decision tree and SVM. The malware dataset is classified as either malicious or benign. This paper unfolds the algorithms’ efficiency by analyzing the hash codes and finds the most efficient algorithm for this particular dataset. Efficiency includes performance metrics. Thus, when the hash codes are fed into the mechanism, it delivers the performance metrics. After finding the perfect algorithm for the dataset, it is used to train the dataset to get the best results." @default.
- W3203077462 created "2021-10-11" @default.
- W3203077462 creator A5044625852 @default.
- W3203077462 creator A5060030834 @default.
- W3203077462 date "2021-01-01" @default.
- W3203077462 modified "2023-10-16" @default.
- W3203077462 title "An Extensive Review on Malware Classification Based on Classifiers" @default.
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- W3203077462 doi "https://doi.org/10.1007/978-981-16-3153-5_40" @default.
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