Name
Title | ||
|---|---|---|
Analysis and Evaluation of Antivirus Engines in Detecting Android Malware: A Data Analytics Approach |
Abstract | ||
|---|---|---|
Given the high popularity of Android devices, the amount of malware applications in Android markets has been growing at a fast pace in the past few years. However, the concept of malware is something vague since it often occurs that AntiVirus engines flag an application as malware while others do not, having no real consensus between different engines. With the help of data analytics applied to more than 80 thousand malware applications, this work further investigates on the relationships between different AntiVirus engines, showing that some of them are highly correlated while others behave totally uncorrelated from others. Finally, we propose a new metric based on Latent Variable Models to identify which engines are more powerful in identifying true malware applications. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/EISIC.2018.00010 | 2018 European Intelligence and Security Informatics Conference (EISIC) |
Keywords | Field | DocType |
Android Malware,Google Play meta-data,Machine Learning,Big Data Analytics | Pace,Android (operating system),Data analysis,Computer security,Computer science,Popularity,Uncorrelated,Android malware,Malware,Big data | Conference |
ISSN | ISBN | Citations |
2572-3723 | 978-1-5386-9401-5 | 0 |
PageRank | References | Authors |
0.34 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ignacio Martín | 1 | 0 | 0.68 |
| José Alberto Hernández | 2 | 131 | 24.49 |
| Sergio de los Santos | 3 | 3 | 1.76 |
| Antonio Guzman | 4 | 9 | 1.53 |