Tree-Based Classifier Ensembles for PE Malware Analysis: A Performance Revisit
| dc.contributor.author | Louk, Maya Hilda Lestari | |
| dc.contributor.author | Tama, Bayu Adhi | |
| dc.date.accessioned | 2022-10-14T15:39:31Z | |
| dc.date.available | 2022-10-14T15:39:31Z | |
| dc.date.issued | 2022-09-17 | |
| dc.description.abstract | Given their escalating number and variety, combating malware is becoming increasingly strenuous. Machine learning techniques are often used in the literature to automatically discover the models and patterns behind such challenges and create solutions that can maintain the rapid pace at which malware evolves. This article compares various tree-based ensemble learning methods that have been proposed in the analysis of PE malware. A tree-based ensemble is an unconventional learning paradigm that constructs and combines a collection of base learners (e.g., decision trees), as opposed to the conventional learning paradigm, which aims to construct individual learners from training data. Several tree-based ensemble techniques, such as random forest, XGBoost, CatBoost, GBM, and LightGBM, are taken into consideration and are appraised using different performance measures, such as accuracy, MCC, precision, recall, AUC, and F1. In addition, the experiment includes many public datasets, such as BODMAS, Kaggle, and CIC-MalMem-2022, to demonstrate the generalizability of the classifiers in a variety of contexts. Based on the test findings, all tree-based ensembles performed well, and performance differences between algorithms are not statistically significant, particularly when their respective hyperparameters are appropriately configured. The proposed tree-based ensemble techniques also outperformed other, similar PE malware detectors that have been published in recent years. | en_US |
| dc.description.sponsorship | This research received no external funding. | en_US |
| dc.description.uri | https://www.mdpi.com/1999-4893/15/9/332 | en_US |
| dc.format.extent | 15 pages | en_US |
| dc.genre | journal articles | en_US |
| dc.identifier | doi:10.13016/m2gzqw-sdku | |
| dc.identifier.citation | Louk, Maya Hilda Lestari, and Bayu Adhi Tama. 2022. "Tree-Based Classifier Ensembles for PE Malware Analysis: A Performance Revisit" Algorithms 15, no. 9: 332. https://doi.org/10.3390/a15090332 | en_US |
| dc.identifier.uri | https://doi.org/10.3390/a15090332 | |
| dc.identifier.uri | http://hdl.handle.net/11603/26183 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | MDPI | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Information Systems Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | en_US |
| dc.rights | Attribution 4.0 International (CC BY 4.0) | * |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
| dc.title | Tree-Based Classifier Ensembles for PE Malware Analysis: A Performance Revisit | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0002-1821-6438 | en_US |