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Mobile malware detection: An analysis of deep learning model

conference contribution
posted on 30.10.2019, 00:00 by ME Khoda, J Kamruzzaman, I Gondal, Tasadduq ImamTasadduq Imam, A Rahman
© 2019 IEEE. Due to its widespread use, with numerous applications deployed everyday, smartphones have become an inevitable target of the malware developers. This huge number of applications renders manual inspection of codes infeasible; as such, researchers have proposed several malware detection techniques based on automatic machine learning tools. Deep learning has gained a lot of attention from the malware researchers due to its ability of capture complex relationships among inputs and outputs. However, deep learning models depend largely on several hyper-parameters (i.e., learning rate, batch size, dropout rate). Hence, it is of utmost importance to analyze the effect of these parameters on classifier performance. In this paper, we systematically studied the effect of these parameters along with the effect of network architecture. We showed that building arbitrary deep networks does not always improve classifier performance. We also determined the combination of hyperparameters that yields best result. This study will be useful in building better deep neural network based model for malware classification.

Funding

Other

History

Start Page

1161

End Page

1166

Number of Pages

6

Start Date

13/02/2019

Finish Date

15/02/2019

ISSN

2641-0184

ISBN-13

9781538663769

Location

Melbourne, Australia

Publisher

IEEE

Place of Publication

Piscataway, NJ

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Federation University, CSIRO

Era Eligible

Yes

Name of Conference

20th IEEE International Conference on Industrial Technology (ICIT 2019)