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Journal: International Journal of Mechanical Engineering and Technology(IJMET) (Vol.10, No. 2)

Publication Date:

Authors : ; ;

Page : 787-797

Keywords : Malware; Malware prediction; K-Nearest Neighbors; Support Vector Machines.;

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The emergence of the vulnerability databases around the world are serving the purpose of a double edged sword. The malware researchers, industry members and end users are aware of them to initiate better prevention strategies. The dark world hackers are using them to lure into systems through the points mentioned in the vulnerability databases. Hence, it is highly necessary to predict the malware at the early stage to avoid further loss. The objective of this research work is to predict the malware using the classifiers Logistic Regression, K–Nearest Neighbors (KNN) and Support Vector Machines (SVM). We found that the appropriate use of these classifiers have resulted great improvement in prediction accuracy. Feature selection is also done to further improve the accuracy to 99% with polynomial kernel function.

Last modified: 2019-05-27 21:53:45