XIII Congreso Internacional de Computación e Informática de Norte de Chile (INFONOR - 2022)

An Empirical Evaluation of Supervised Learning Methods for Network Malware Identification Based on Feature Selection

30 Sept 2022, 11:20

20m

Sala 1 (VC Reuna - UNAP)

VI Workshop on Data And Knowledge Engineering (Wdke 2022)

Speaker

Mr Caelos Manzano Munizaga (Escuela de Ingeniería, Universidad Católica del Norte, Coquimbo, Chile.)

Description

Malware is a sophisticated, malicious, and sometimes unidentifiable application on the network. The classifying network traffic method using machine learning shows to perform well in detecting malware. In the literature, it is reported that this good performance can depend on a reduced set of network features. This study presents the evaluation of two statistical methods of reduction and selection of features in an Android network traffic dataset using six well-known supervised machine learning algorithms.