DocumentCode :
3658054
Title :
Potential Component Leaks in Android Apps: An Investigation into a New Feature Set for Malware Detection
Author :
Li Li;Kevin Allix;Daoyuan Li;Alexandre Bartel;Tegawendé F. Bissyandé;Jacques Klein
Author_Institution :
SnT, Univ. of Luxembourg, Luxembourg, Luxembourg
fYear :
2015
Firstpage :
195
Lastpage :
200
Abstract :
We discuss the capability of a new feature set for malware detection based on potential component leaks (PCLs). PCLs are defined as sensitive data-flows that involve Android inter-component communications. We show that PCLs are common in Android apps and that malicious applications indeed manipulate significantly more PCLs than benign apps. Then, we evaluate a machine learning-based approach relying on PCLs. Experimental validations show high performance for identifying malware, demonstrating that PCLs can be used for discriminating malicious apps from benign apps.
Keywords :
"Malware","Androids","Humanoid robots","Feature extraction","Libraries","Machine learning algorithms","Training"
Publisher :
ieee
Conference_Titel :
Software Quality, Reliability and Security (QRS), 2015 IEEE International Conference on
Type :
conf
DOI :
10.1109/QRS.2015.36
Filename :
7272932
Link To Document :
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