Name
Title | ||
|---|---|---|
Idea: Automatic Localization of Malicious Behaviors in Android Malware with Hidden Markov Models. |
Abstract | ||
|---|---|---|
The lack of ground truth about malicious behaviors exhibited by current Android malware forces researchers to embark upon a lengthy process of manually analyzing malware instances. In this paper, we propose a method to automatically localize malicious behaviors residing in representations of apps' runtime behaviors. Our initial evaluation using generated API calls traces of Android apps demonstrates the method's feasibility and applicability. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-94496-8_8 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Android (operating system),Computer security,Computer science,Android malware,Ground truth,Hidden Markov model,Malware | Conference | 10953 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aleieldin Salem | 1 | 1 | 1.71 |
| Tabea Schmidt | 2 | 0 | 0.34 |
| Alexander Pretschner | 3 | 19 | 4.74 |