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
Hlavní autoři: Guzmán, Antonio, Muñoz, Alfonso, Hernández, José Alberto, Martín, Ignacio
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018
Hindawi
John Wiley & Sons, Inc
Témata:
Accuracy
Applications programs
Classification
Datasets
Inspection
Machine learning
Malware
Metadata
Smartphones
Support vector machines
Tablet computers
Trends
User behavior
ISSN:1939-0114, 1939-0122
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1939-0114
1939-0122
DOI:10.1155/2018/5749481