@conference {Chemmakha2022771,
	title = {Improving Machine Learning Models for Malware Detection Using Embedded Feature Selection Method},
	booktitle = {IFAC-PapersOnLine},
	volume = {55},
	number = {12},
	year = {2022},
	note = {cited By 0},
	pages = {771-776},
	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). {\textcopyright} 2022 Elsevier B.V.. All rights reserved.},
	keywords = {ANN, 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},
	doi = {10.1016/j.ifacol.2022.07.406},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137157154\&doi=10.1016\%2fj.ifacol.2022.07.406\&partnerID=40\&md5=4c66048ed61a71abdde00c73a3839bbc},
	author = {Chemmakha, M. and Habibi, O. and Lazaar, M.}
}
