A malicious encrypted traffic detection method based on hybrid feature selection

Abstract

To overcome the challenges of high feature redundancy, strong subjectivity in manual feature selection, and low model adaptability in malicious encrypted traffic, this paper proposes a hybrid feature selection method based on mutual information and an improved Harris hawk optimization algorithm. This method combines the advantages of filters and wrappers, selects important features in malicious encrypted traffic through two-stage screening, and inputs them into a deep learning model for classification. Experimental results demonstrate that this method can select higher quality feature subsets and improve the classification ability of malicious encrypted traffic detection models.