Improving Machine Learning Models for Malware Detection Using Embedded Feature Selection Method

TitreImproving Machine Learning Models for Malware Detection Using Embedded Feature Selection Method
Publication TypeConference Paper
Year of Publication2022
AuthorsChemmakha, M, Habibi, O, Lazaar, M
Conference NameIFAC-PapersOnLine
Mots-clésANN, Decision trees, feature selection, Features selection, Lightgbm, Machine learning models, Machine-learning, Malware, Malware detection, Random forests, support vector machine, Support vector machines, Support vectors machine, Xgboost
Abstract

Machine learning performance always rely on relevant phase of pre-processing, that includes dataset cleaning, cleansing and extraction. Feature selection (FS) is a crucial phase too, because it is intended to increase the efficiency of Machine Learning (ML) models in terms of predictiveness, by assigning a representative value to the most important features in a dataset of malware. In this study, we focus on feature selection using embedded-based methods in order to minimize computational time and complexity of ML models. Embedded-based methods combine advantages of both filter-based and wrapped-based methods, in terms of studying the importance of features while executing the model and their reduced time of execution. Applying ML models shows a high stability of models will selecting 10 most relevant features from the dataset, with an accuracy that achieve 99.47%, 99.02% for respectively Random Forest (RF) and XGBoost (XGB). © 2022 Elsevier B.V.. All rights reserved.

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85137157154&doi=10.1016%2fj.ifacol.2022.07.406&partnerID=40&md5=4c66048ed61a71abdde00c73a3839bbc
DOI10.1016/j.ifacol.2022.07.406
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