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Evolutionary Intelligence
Published in: Evolutionary Intelligence 1/2022

12-11-2020 | Research Paper

SOMDROID: android malware detection by artificial neural network trained using unsupervised learning

Authors: Arvind Mahindru, A. L. Sangal

Published in: Evolutionary Intelligence | Issue 1/2022

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Abstract

Android has gained its popularity due to its open-source and number of freely available apps in its official play store. Appropriate functioning of Android apps depends upon the permission or set of permissions which an app demands at the time of installation and run-time. By taking the advantage of these permissions or set of permissions, cybercriminals are developing malware-infected apps daily. In this study, we proposed a framework named as “SOMDROID”, that work on the principle of unsupervised machine learning algorithm. To develop an effective and efficient Android malware detection model, we collect 5,00,000 distinct Android apps from promised repositories and extract 1844 unique features. Further, to select significant features or feature sets, we applied six different feature ranking approaches in this study. With the selected feature or feature sets, we implement the Self-Organizing Map (SOM) algorithm of Kohonen and measure four distinct performance parameters, i.e., Intra-cluster distance, Inter-cluster distance, Accuracy and F-measure. Empirical result reveals that our proposed framework is able to detect 98.7% malware that belongs to unknown families and in addition to that the detection rate is higher by 2% when compared to commercial anti-virus scanners and frameworks proposed in the literature.
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Appendix
Available only for authorised users
Footnotes
7
Mahindru, Arvind (2020), “Android permissions data set, Android Malware, and benign Application Data set (consist of permissions and API calls)”, Mendeley Data, v3 http://​dx.​doi.​org/​10.​17632/​b4mxg7ydb7.​3.
 
12
Training parameters consider to train the SOM are as follows: value of \(\alpha\) for rough training is “0.5” and for fine tuning it is “0.05”. Starting neighborhood size is “3” for rough training and “1” for fine tuning. Size of the map consider in this study is 10 x 10. Gaussian and Hexagonal are considered as neighborhood function and relation respectively for both the rough and fine tuning. Value of \(\alpha\) scheme is set to be \(inverser_t\) and epoch limit is set to be 2000 in this study.
 
14
For calculating Accuracy and F-measure, we use traditional methods that were available in the literature having 100% labelled data set. But, in the case of SOM, it is unlabelled data set.
 
15
Experiment was performed on only 2,00,000 Android apps, obtained on the basis of user rating .
 
16
For this experiment, the size of the Android app is less than 50 MB.
 
17
Experiment was performed on Android apps having a size less than 50 MB.
 
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Metadata
Title
SOMDROID: android malware detection by artificial neural network trained using unsupervised learning
Authors
Arvind Mahindru
A. L. Sangal
Publication date
12-11-2020
Publisher
Springer Berlin Heidelberg
Published in
Evolutionary Intelligence / Issue 1/2022
Print ISSN: 1864-5909
Electronic ISSN: 1864-5917
DOI
https://doi.org/10.1007/s12065-020-00518-1

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