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2017 | OriginalPaper | Chapter

Android Malware Detection Using Hybrid Analysis and Machine Learning Technique

Authors : Fan Yang, Yi Zhuang, Jun Wang

Published in: Cloud Computing and Security

Publisher: Springer International Publishing

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Abstract

This paper proposes a two-stage Android malware detection and classification mechanism based on machine learning algorithm. In this paper, we use the static analysis method to extract the software’s package features, permission features, component features and triggering mechanism. Then we use the dynamic analysis tools to obtain the dynamic behavior characters of the software, and format the static and dynamic features. Finally, we use the machine learning algorithm to deal with the feature eigenvectors in two stages, and then we will get the malicious classification of the software. The experimental results show that in the data set used in this paper the proposed method based on the combination of dynamic and static malicious code detection is more accurate than the common detection engine, and the ability of classifying malicious family is much stronger.

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Metadata
Title
Android Malware Detection Using Hybrid Analysis and Machine Learning Technique
Authors
Fan Yang
Yi Zhuang
Jun Wang
Copyright Year
2017
Book
Cloud Computing and Security

Print ISBN: 978-3-319-68541-0

Electronic ISBN: 978-3-319-68542-7

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-68542-7_48

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