Delving into Android Malware Families with a Novel Neural Projection Method

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ID: 39505
2019
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Abstract
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.
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vega2019delvingcomplexity Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Vega, Rafael Vega;Quintián, Héctor;Cambra, Carlos;Basurto, Nuño;Herrero, Álvaro;Calvo-Rolle, José Luis;
Journal complexity
Year 2019
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URL
http://dx.doi.org/10.1155/2019/6101697
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