Name
Abstract | ||
|---|---|---|
The relevance of malicious software targeting mobile devices has been increasing in recent years. Smartphones, tablet computers or embedded devices in general represent one of the most spread computing platform worldwide and an unsecure usage can cause unprecedented damage to private users, companies and public institutions. To help in identifying malicious software on mobile platforms, we propose RAMSES, an approach based on the static content stored as strings within an application. First we extract the contents of strings, transforming applications into documents, then using information retrieval techniques, we select the most relevant features based on frequency metrics, and finally we classify applications using machine learning algorithms relying on such features. We evaluate our methods using real datasets of Android applications and show promising results for detection. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-23829-6_34 | Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering |
Keywords | Field | DocType |
Android,Malware,Static analysis,Detection,Security | Android (operating system),Computer security,Computer science,Static analysis,Android malware,Mobile device,Malware | Conference |
Volume | ISSN | Citations |
152 | 1867-8211 | 1 |
PageRank | References | Authors |
0.34 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lautaro Dolberg | 1 | 21 | 3.60 |
| Quentin Jérome | 2 | 39 | 2.97 |
| Jérôme François | 3 | 170 | 21.81 |
| Radu State | 4 | 623 | 86.87 |
| Thomas Engel | 5 | 455 | 42.34 |