Name
Title | ||
|---|---|---|
Detection Of Android Applications With Malicious Behavior Based On Sparse Bayesian Learning Algorithm |
Abstract | ||
|---|---|---|
Android mobile devices are widely used in recent years. Due to the openness of Android, applications with malicious behavior have more opportunities to get confidential information, which can cause property damage. Most of current solutions are hard to detect these rapidly developing malicious applications with high accuracy. In this paper, a static malicious application detection method based on Sparse Bayesian Learning Algorithm and n-gram analysis is proposed to solve this problem. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-00018-9_24 | CLOUD COMPUTING AND SECURITY, PT V |
Keywords | Field | DocType |
Malware detection, Android, N-gram, Sparse Bayesian Learning Algorithm, Dalvik opcode | Android (operating system),Bayesian inference,Confidentiality,Computer science,Algorithm,Mobile device,n-gram,Distributed computing | Conference |
Volume | ISSN | Citations |
11067 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 13 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ning Liu | 1 | 0 | 0.34 |
| Yang, M. | 2 | 25 | 17.85 |
| hang zhang | 3 | 31 | 16.05 |
| Chen Yang | 4 | 0 | 1.35 |
| Yang Zhao | 5 | 836 | 116.78 |
| Jianchao Gan | 6 | 0 | 0.34 |
| Shibin Zhang | 7 | 2 | 2.05 |