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Feature Subset Selection for Malware Detection in Smart IoT Platforms
Malicious software (“malware”) has become one of the serious cybersecurity issues in Android ecosystem. Given the fast evolution of Android malware releases, it is practically not feasible to manually detect malware apps in the Android ecosystem. As a result, machine learning has become a fledgling...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7919840/ https://www.ncbi.nlm.nih.gov/pubmed/33669191 http://dx.doi.org/10.3390/s21041374 |
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author | Abawajy, Jemal Darem, Abdulbasit Alhashmi, Asma A. |
author_facet | Abawajy, Jemal Darem, Abdulbasit Alhashmi, Asma A. |
author_sort | Abawajy, Jemal |
collection | PubMed |
description | Malicious software (“malware”) has become one of the serious cybersecurity issues in Android ecosystem. Given the fast evolution of Android malware releases, it is practically not feasible to manually detect malware apps in the Android ecosystem. As a result, machine learning has become a fledgling approach for malware detection. Since machine learning performance is largely influenced by the availability of high quality and relevant features, feature selection approaches play key role in machine learning based detection of malware. In this paper, we formulate the feature selection problem as a quadratic programming problem and analyse how commonly used filter-based feature selection methods work with emphases on Android malware detection. We compare and contrast several feature selection methods along several factors including the composition of relevant features selected. We empirically evaluate the predictive accuracy of the feature subset selection algorithms and compare their predictive accuracy and the execution time using several learning algorithms. The results of the experiments confirm that feature selection is necessary for improving accuracy of the learning models as well decreasing the run time. The results also show that the performance of the feature selection algorithms vary from one learning algorithm to another and no one feature selection approach performs better than the other approaches all the time. |
format | Online Article Text |
id | pubmed-7919840 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79198402021-03-02 Feature Subset Selection for Malware Detection in Smart IoT Platforms Abawajy, Jemal Darem, Abdulbasit Alhashmi, Asma A. Sensors (Basel) Article Malicious software (“malware”) has become one of the serious cybersecurity issues in Android ecosystem. Given the fast evolution of Android malware releases, it is practically not feasible to manually detect malware apps in the Android ecosystem. As a result, machine learning has become a fledgling approach for malware detection. Since machine learning performance is largely influenced by the availability of high quality and relevant features, feature selection approaches play key role in machine learning based detection of malware. In this paper, we formulate the feature selection problem as a quadratic programming problem and analyse how commonly used filter-based feature selection methods work with emphases on Android malware detection. We compare and contrast several feature selection methods along several factors including the composition of relevant features selected. We empirically evaluate the predictive accuracy of the feature subset selection algorithms and compare their predictive accuracy and the execution time using several learning algorithms. The results of the experiments confirm that feature selection is necessary for improving accuracy of the learning models as well decreasing the run time. The results also show that the performance of the feature selection algorithms vary from one learning algorithm to another and no one feature selection approach performs better than the other approaches all the time. MDPI 2021-02-16 /pmc/articles/PMC7919840/ /pubmed/33669191 http://dx.doi.org/10.3390/s21041374 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Abawajy, Jemal Darem, Abdulbasit Alhashmi, Asma A. Feature Subset Selection for Malware Detection in Smart IoT Platforms |
title | Feature Subset Selection for Malware Detection in Smart IoT Platforms |
title_full | Feature Subset Selection for Malware Detection in Smart IoT Platforms |
title_fullStr | Feature Subset Selection for Malware Detection in Smart IoT Platforms |
title_full_unstemmed | Feature Subset Selection for Malware Detection in Smart IoT Platforms |
title_short | Feature Subset Selection for Malware Detection in Smart IoT Platforms |
title_sort | feature subset selection for malware detection in smart iot platforms |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7919840/ https://www.ncbi.nlm.nih.gov/pubmed/33669191 http://dx.doi.org/10.3390/s21041374 |
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