Statistical Learning Applied to Malware Detection - Rodríguez Aguilar, Román; Marmolejo Saucedo, José Antonio (My University, 2020)
This work shows an application of statistical learning methodologies in order to determine the important factors for malware detection. Support Vector Machines and Lasso Regression performed Malware classification with additional re-sampling methods. The results show that the Lasso Regression allows an efficient selection of relevant variables for the construction of the classifier, also the integration of support vector machines improves the efficiency of the classifier through the application of resampling methods. The model presented in this paper uses a statistical learning approach through the selection of variables, non-linear classification, and resampling methods. © 2020, Springer Nature Switzerland AG.
Keywords: Lasso regression; Malware detection; Resampling; Statistical learning; Support vector machine