A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
On lightweight mobile phone application certification
Proceedings of the 16th ACM conference on Computer and communications security
Proceedings of the 17th ACM conference on Computer and communications security
Paranoid Android: versatile protection for smartphones
Proceedings of the 26th Annual Computer Security Applications Conference
TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones
OSDI'10 Proceedings of the 9th USENIX conference on Operating systems design and implementation
A study of android application security
SEC'11 Proceedings of the 20th USENIX conference on Security
Crowdroid: behavior-based malware detection system for Android
Proceedings of the 1st ACM workshop on Security and privacy in smartphones and mobile devices
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Android has become one of the most popular mobile operating system because of numerous applications it provides. Android Market is the official application store which allows users to search and install applications to their Android devices. However, with the increasingly number of applications, malware is also beginning to turn up in app stores. To mitigate the security problem brought by malware, we put forward a novel permission-based abnormal application detection framework which identifies potentially dangerous apps by the reliability of their permission lists. To judge the reliability of app's permissions, we make use of the relation between app's description text and its permission list. In detail, we use Naive Bayes with Multinomial Event Model algorithm to build the relation between the description and the permission list of an application. We evaluate this framework with 5,685 applications in Android Market and find it effective in identifying abnormal application in Android Market.