Delving into Android Malware Families with a Novel Neural Projection Method
Joint Authors
Vega Vega, Rafael
Quintián, Héctor
Cambra, Carlos
Basurto, Nuño
Herrero, Álvaro
Calvo-Rolle, José Luis
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-06-02
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Present research proposes the application of unsupervised and supervised machine-learning techniques to characterize Android malware families.
More precisely, a novel unsupervised neural-projection method for dimensionality-reduction, namely, Beta Hebbian Learning (BHL), is applied to visually analyze such malware.
Additionally, well-known supervised Decision Trees (DTs) are also applied for the first time in order to improve characterization of such families and compare the original features that are identified as the most important ones.
The proposed techniques are validated when facing real-life Android malware data by means of the well-known and publicly available Malgenome dataset.
Obtained results support the proposed approach, confirming the validity of BHL and DTs to gain deep knowledge on Android malware.
American Psychological Association (APA)
Vega Vega, Rafael& Quintián, Héctor& Cambra, Carlos& Basurto, Nuño& Herrero, Álvaro& Calvo-Rolle, José Luis. 2019. Delving into Android Malware Families with a Novel Neural Projection Method. Complexity،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1132341
Modern Language Association (MLA)
Vega Vega, Rafael…[et al.]. Delving into Android Malware Families with a Novel Neural Projection Method. Complexity No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1132341
American Medical Association (AMA)
Vega Vega, Rafael& Quintián, Héctor& Cambra, Carlos& Basurto, Nuño& Herrero, Álvaro& Calvo-Rolle, José Luis. Delving into Android Malware Families with a Novel Neural Projection Method. Complexity. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1132341
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1132341