Android Malware Characterization Using Metadata and Machine Learning Techniques
Joint Authors
Martín, Ignacio
Hernández, José Alberto
Muñoz, Alfonso
Guzmán, Antonio
Source
Security and Communication Networks
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-07-08
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Martín, Ignacio& Hernández, José Alberto& Muñoz, Alfonso& Guzmán, Antonio. 2018. Android Malware Characterization Using Metadata and Machine Learning Techniques. Security and Communication Networks،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1214232
Modern Language Association (MLA)
Martín, Ignacio…[et al.]. Android Malware Characterization Using Metadata and Machine Learning Techniques. Security and Communication Networks No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1214232
American Medical Association (AMA)
Martín, Ignacio& Hernández, José Alberto& Muñoz, Alfonso& Guzmán, Antonio. Android Malware Characterization Using Metadata and Machine Learning Techniques. Security and Communication Networks. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1214232
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214232