@conference {Habibi2022462,
	title = {Effect of Features Extraction and Selection on the Evaluation of Machine Learning Models},
	booktitle = {IFAC-PapersOnLine},
	volume = {55},
	number = {12},
	year = {2022},
	note = {cited By 0},
	pages = {462-467},
	abstract = {The exponential growth of sophisticated malware attacks against computer systems, has alerted IT security experts on the shortcomings of traditional protection tools because, they became unable to detect new families of malware that are more advanced and use advanced tools such as polymorphism, metamorphism, and obfuscation tools. Nowadays, machine learning is widely used in several IT fields; and also in cybersecurity, and can be an essential tool for malware detection, moreover, it can go beyond the limits of classic malware detection methods, such as the signature-based method, the anomaly-based method and the hybrid-based method, etc. The purpose of this study is to analyze the feature selection and extraction effects on the performance of malware classification model using machine learning. The results show that reducing dimensionality of datasets can help to improve the efficiency of security models in restricted time but with high performance, Random Forest using chi-square achieves an accuracy of 99.51\%. {\textcopyright} 2022 Elsevier B.V.. All rights reserved.},
	keywords = {ANN, Cybersecurity, Decision trees, Extraction, Feature extraction, Features extraction, KNN, Logistic regression, Logistics regressions, Machine-learning, Malware, Malware detection, Network security, Performance, random forest, Random forests},
	doi = {10.1016/j.ifacol.2022.07.355},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137165053\&doi=10.1016\%2fj.ifacol.2022.07.355\&partnerID=40\&md5=b3575cc26a69761120b672e3d94e63c0},
	author = {Habibi, O. and Chemmakha, M. and Lazaar, M.}
}
@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.}
}
