DocumentCode :
3733993
Title :
Using association statistics to rank risk of Android application
Author :
Chenkai Guo;Jing Xu;Lei Liu;Sihan Xu
Author_Institution :
College of Computer and Control Engineering, Nankai University, Tianjin, China
fYear :
2015
Firstpage :
6
Lastpage :
10
Abstract :
With the development of Android system and intelligent mobile technology, Android applications have been widely used in recent years. However, the trust on these applications may be used by attackers who design malware to achieve illegal income. Therefore, to evaluate the security risk of applications is necessary. In this paper, an evaluation approach based on association statistics of application permissions is represented. By analyzing the frequency of permission combinations, the malicious features of Android applications is estimated. Meanwhile, the redundant frequency is wiped off by computing the frequency discount, and then the risk seeds are correspondingly achieved. The final risk score is calculated by the seeds from malicious and benign aspects. To verify the effectiveness of our approach, we took 1260 malware as "malicious" datasets and 10,247 valid apps collected from Android Market as "benign" datasets, and sufficient experiments show that our approach has better results compared with other traditional methods.
Keywords :
"Malware","Smart phones","Feature extraction","Classification algorithms","Risk management","Androids"
Publisher :
ieee
Conference_Titel :
Computer and Communications (ICCC), 2015 IEEE International Conference on
Print_ISBN :
978-1-4673-8125-3
Type :
conf
DOI :
10.1109/CompComm.2015.7387530
Filename :
7387530
Link To Document :
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