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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Veröffentlicht in:Security and communication networks Jg. 2018; H. 2018; S. 1 - 11
Hauptverfasser: Guzmán, Antonio, Muñoz, Alfonso, Hernández, José Alberto, Martín, Ignacio
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018
Hindawi
John Wiley & Sons, Inc
Schlagworte:
Accuracy
Applications programs
Classification
Datasets
Inspection
Machine learning
Malware
Metadata
Smartphones
Support vector machines
Tablet computers
Trends
User behavior
ISSN:1939-0114, 1939-0122
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1939-0114
1939-0122
DOI:10.1155/2018/5749481