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Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation)
Android is a free open-source operating system (OS), which allows an in-depth understanding of its architecture. Therefore, many manufacturers are utilizing this OS to produce mobile devices (smartphones, smartwatch, and smart glasses) in different brands, including Google Pixel, Motorola, Samsung,...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8594292/ https://www.ncbi.nlm.nih.gov/pubmed/34825052 http://dx.doi.org/10.7717/peerj-cs.522 |
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author | Jusoh, Rosmalissa Firdaus, Ahmad Anwar, Shahid Osman, Mohd Zamri Darmawan, Mohd Faaizie Ab Razak, Mohd Faizal |
author_facet | Jusoh, Rosmalissa Firdaus, Ahmad Anwar, Shahid Osman, Mohd Zamri Darmawan, Mohd Faaizie Ab Razak, Mohd Faizal |
author_sort | Jusoh, Rosmalissa |
collection | PubMed |
description | Android is a free open-source operating system (OS), which allows an in-depth understanding of its architecture. Therefore, many manufacturers are utilizing this OS to produce mobile devices (smartphones, smartwatch, and smart glasses) in different brands, including Google Pixel, Motorola, Samsung, and Sony. Notably, the employment of OS leads to a rapid increase in the number of Android users. However, unethical authors tend to develop malware in the devices for wealth, fame, or private purposes. Although practitioners conduct intrusion detection analyses, such as static analysis, there is an inadequate number of review articles discussing the research efforts on this type of analysis. Therefore, this study discusses the articles published from 2009 until 2019 and analyses the steps in the static analysis (reverse engineer, features, and classification) with taxonomy. Following that, the research issue in static analysis is also highlighted. Overall, this study serves as the guidance for novice security practitioners and expert researchers in the proposal of novel research to detect malware through static analysis. |
format | Online Article Text |
id | pubmed-8594292 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85942922021-11-24 Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation) Jusoh, Rosmalissa Firdaus, Ahmad Anwar, Shahid Osman, Mohd Zamri Darmawan, Mohd Faaizie Ab Razak, Mohd Faizal PeerJ Comput Sci Artificial Intelligence Android is a free open-source operating system (OS), which allows an in-depth understanding of its architecture. Therefore, many manufacturers are utilizing this OS to produce mobile devices (smartphones, smartwatch, and smart glasses) in different brands, including Google Pixel, Motorola, Samsung, and Sony. Notably, the employment of OS leads to a rapid increase in the number of Android users. However, unethical authors tend to develop malware in the devices for wealth, fame, or private purposes. Although practitioners conduct intrusion detection analyses, such as static analysis, there is an inadequate number of review articles discussing the research efforts on this type of analysis. Therefore, this study discusses the articles published from 2009 until 2019 and analyses the steps in the static analysis (reverse engineer, features, and classification) with taxonomy. Following that, the research issue in static analysis is also highlighted. Overall, this study serves as the guidance for novice security practitioners and expert researchers in the proposal of novel research to detect malware through static analysis. PeerJ Inc. 2021-06-11 /pmc/articles/PMC8594292/ /pubmed/34825052 http://dx.doi.org/10.7717/peerj-cs.522 Text en ©2021 Jusoh et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Jusoh, Rosmalissa Firdaus, Ahmad Anwar, Shahid Osman, Mohd Zamri Darmawan, Mohd Faaizie Ab Razak, Mohd Faizal Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation) |
title | Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation) |
title_full | Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation) |
title_fullStr | Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation) |
title_full_unstemmed | Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation) |
title_short | Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation) |
title_sort | malware detection using static analysis in android: a review of feco (features, classification, and obfuscation) |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8594292/ https://www.ncbi.nlm.nih.gov/pubmed/34825052 http://dx.doi.org/10.7717/peerj-cs.522 |
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