Conference Paper

Android Malware Detection Using Stacked Generalization

2018 International Conference on Software Engineering and Data Engineering
Md. Shohel Rana, Charan Gudla, Andrew H. Sung

ABSTRACT


Malware detection plays a key role in Android device

security due to the popularity of Android with billions

of active users that encouraging cybercriminals to push

the malware into this operating system. The growth of

malware is now becoming a serious problem. Recently,

extensive research has been conducted to detect malware

on Android devices using machine learning based

methods profoundly depending on domain knowledge for

manually extracting malicious features. In this paper,

we evaluate tree-based machine learning algorithms by

Stacked Generalization concept for detecting malware on

Android in conjunction with implementing a substringbased

method for training the algorithms. We perform

experiments on 11,120 samples containing 5,560 malware

samples and 5,560 benign samples provided by

DREBIN dataset on 8 malware families. The evaluation

results show how stacked generalization achieves 97.92%

validation accuracy for malware detection on DREBIN

dataset.

SEDE 2018



ISBN:
978-1-943436-05-7
PUBLISHER:
ISCA
CHIEF EDITOR:
Frederick C. Harris Jr. Yan Shi Sergiu Dascalu
CONFERENCE VENUE:
Denver Colorado USA
CONTACT DETAILS:
Debnath
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