Name
Abstract | ||
|---|---|---|
The increasing popularity of Android apps makes them the target of malware authors. To defend against this severe increase of Android malwares and help users make a better evaluation of apps at install time, several approaches have been proposed. However, most of these solutions suffer from some shortcomings; computationally expensive, not general or not robust enough. In this paper, we aim to mitigate Android malware installation through providing robust and lightweight classifiers. We have conducted a thorough analysis to extract relevant features to malware behavior captured at API level, and evaluated different classifiers using the generated feature set. Our results show that we are able to achieve an accuracy as high as 99% and a false positive rate as low as 2.2% using KNN classifier. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-319-04283-1_6 | Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering |
Keywords | Field | DocType |
Android,malware,static detection,classification | False positive rate,Android (operating system),Computer security,Computer science,Popularity,Android malware,Feature set,Malware,Classifier (linguistics) | Conference |
Volume | ISSN | Citations |
127 | 1867-8211 | 163 |
PageRank | References | Authors |
4.17 | 16 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yousra Aafer | 1 | 264 | 13.36 |
| wenliang du | 2 | 4906 | 241.77 |
| Heng Yin | 3 | 2153 | 111.33 |