Shikra: A Behavior-Based Android Malware Detection Framework

With the growth of Android platform malicious application, the seperation of malicious application from nonmalicious application has become challenging. In recent years, a combination of static analysis and dynamic analysis of the idea is very popular. However, it is very costly for dynamic analysis...

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Bibliographic Details
Published in2017 International Conference on Green Informatics (ICGI) pp. 175 - 184
Main Authors Ma, Zhao-hui, Chen, Zi-hao, Wang, Xin-ming, Nie, Rui-hua, Zhao, Gan-sen, Wu, Jie-chao, Ren, Xue-qi
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2017
Subjects
Algorithm design and analysis
android
Androids
behavior
Classification algorithms
Humanoid robots
Malicious detection
Smart phones
Support vector machines
SVM
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Summary:With the growth of Android platform malicious application, the seperation of malicious application from nonmalicious application has become challenging. In recent years, a combination of static analysis and dynamic analysis of the idea is very popular. However, it is very costly for dynamic analysis to achieve high coverage. In this article we present an efficient, lightweight and behavior-based architecture based on the behavior of malicious developers. Information and behavior preferences of malicious developers in binary code are collected. And through the multi-plane SVM Android application malicious and non-malicious division.
DOI:10.1109/ICGI.2017.35