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  • 标题:Improving Machine Learning Models for Malware Detection Using Embedded Feature Selection Method
  • 本地全文:下载
  • 作者:Mohammed CHEMMAKHA ; Omar HABIBI ; Mohamed LAZAAR
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2022
  • 卷号:55
  • 期号:12
  • 页码:771-776
  • DOI:10.1016/j.ifacol.2022.07.406
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractMachine 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).
  • 关键词:KeywordsFeature SelectionMachine LearningMalware DetectionLightGBMRandom ForestSupport vector machine (SVM)ANNXGBoost
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