Android Malware Characterization Using Metadata and Machine Learning Techniques
Android malware has emerged as a consequence of the increasing popularity of smartphones and tablets. While most previous work focuses on inherent characteristics of Android apps to detect malware, this study analyses indirect features and metadata to identify patterns in malware applications. Our e...
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| Vydáno v: | Security and communication networks Ročník 2018; číslo 2018; s. 1 - 11 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Cairo, Egypt
Hindawi Publishing Corporation
01.01.2018
Hindawi John Wiley & Sons, Inc |
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| ISSN: | 1939-0114, 1939-0122 |
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| Abstract | Android malware has emerged as a consequence of the increasing popularity of smartphones and tablets. While most previous work focuses on inherent characteristics of Android apps to detect malware, this study analyses indirect features and metadata to identify patterns in malware applications. Our experiments show the following: (1) the permissions used by an application offer only moderate performance results; (2) other features publicly available at Android markets are more relevant in detecting malware, such as the application developer and certificate issuer; and (3) compact and efficient classifiers can be constructed for the early detection of malware applications prior to code inspection or sandboxing. |
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| AbstractList | Android malware has emerged as a consequence of the increasing popularity of smartphones and tablets. While most previous work focuses on inherent characteristics of Android apps to detect malware, this study analyses indirect features and metadata to identify patterns in malware applications. Our experiments show the following: (1) the permissions used by an application offer only moderate performance results; (2) other features publicly available at Android markets are more relevant in detecting malware, such as the application developer and certificate issuer; and (3) compact and efficient classifiers can be constructed for the early detection of malware applications prior to code inspection or sandboxing. |
| Author | Hernández, José Alberto Martín, Ignacio Muñoz, Alfonso Guzmán, Antonio |
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| Cites_doi | 10.1016/j.cose.2014.11.001 10.1016/j.cose.2016.11.007 10.1016/j.cose.2013.02.002 10.15394/jdfsl.2013.1149 10.1049/iet-ifs.2014.0099 10.1016/j.pmcj.2016.06.018 10.1049/iet-ifs.2013.0095 10.1155/2008/712353 10.1007/s10844-010-0148-x 10.1007/978-3-319-04283-1_6 10.1007/978-3-642-33018-6_30 10.1007/978-1-4614-7138-7 |
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| Copyright | Copyright © 2018 Ignacio Martín et al. Copyright © 2018 Ignacio Martín et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| SubjectTerms | Accuracy Applications programs Classification Datasets Inspection Machine learning Malware Metadata Smartphones Support vector machines Tablet computers Trends User behavior |
| Title | Android Malware Characterization Using Metadata and Machine Learning Techniques |
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