Gaining deep knowledge of Android malware families through dimensionality reduction techniques. (11th September 2018)

• Record Type: Journal Article
• Authors: Vega Vega, Rafael; Quintián, Héctor; Calvo-Rolle, José Luís; Herrero, Álvaro; Corchado, Emilio
• Is Part Of: Logic journal of the IGPL. Volume 27:Number 2(2019)
• Page Start: 160
• Page End: 176
• Publication Date: 2018-09-11
• Subjects: Android malware -- malware families -- dimensionality reduction -- artificial neural networks; Logic, Symbolic and mathematical -- Periodicals; 511.3
• DOI: 10.1093/jigpal/jzy030
• Languages: English
• ISSNs: 1367-0751

Abstract

Abstract: This research proposes the analysis and subsequent characterisation of Android malware families by means of low dimensional visualisations using dimensional reduction techniques. The well-known Malgenome data set, coming from the Android Malware Genome Project, has been thoroughly analysed through the following six dimensionality reduction techniques: Principal Component Analysis, Maximum Likelihood Hebbian Learning, Cooperative Maximum Likelihood Hebbian Learning, Curvilinear Component Analysis, Isomap and Self Organizing Map. Results obtained enable a clear visual analysis of the structure of this high-dimensionality data set, letting us gain deep knowledge about the nature of such Android malware families. Interesting conclusions are obtained from the real-life data set under analysis.