Name
Abstract | ||
|---|---|---|
The rapid growing of the Android application and malware has increased the usage of the application category in Android malware detection and application searching. However, the defects of management of Android Market lead to a great deal of applications miscategorization. Therefore, it's helpful for both organizing the Android Market and Android malware detection to give an approach that can automatically distinguish different categories of the applications. In this paper, we present an effective approach for automatically categorizing Android applications based on Bayesian classification. Considering the category of the application is determined by its function, we extracted the used permissions and strings that can reflect the application function from the application itself and Android Market as classification features. Finally, we conduct experiments with 13005 applications that are composed of 18 categories with Naive Bayes. The evaluation results show that our approach can achieve better accuracy and performance than previous coarse-grained feature extraction methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CyberC.2016.42 | 2016 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) |
Keywords | Field | DocType |
Android category,malware detection,Bayesian classification,permission | Categorization,Data mining,Android (operating system),Naive Bayes classifier,Computer science,Android malware,Feature extraction,Artificial intelligence,Malware,Machine learning,Mobile telephony,Humanoid robot | Conference |
ISBN | Citations | PageRank |
978-1-5090-5155-7 | 0 | 0.34 |
References | Authors | |
3 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cangzhou Yuan | 1 | 1 | 1.79 |
| Shenhong Wei | 2 | 0 | 0.68 |
| Yutong Wang | 3 | 0 | 0.34 |
| Yue You | 4 | 8 | 1.36 |
| ShangGuan ZiLiang | 5 | 0 | 0.34 |