Name
Abstract | ||
|---|---|---|
Due to the delay of threat warning and vulnerability fixing, the critical servers in cyberspace are under potential threat. With the help of vulnerability detection system, we can reduce risk and manage servers efficiently. To date, substantial related works have been done, combined with unenjoyable performance. To address these issues, we present VulAware, which is a distributed framework for detecting vulnerabilities. It is able to detect remote vulnerabilities automatically. Finally, empirical results show that VulAware significantly outperforms the state-of-the-art methods in both speed and robustness. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-00557-3_15 | MLICOM |
Field | DocType | Citations |
Vulnerability (computing),Computer security,Computer science,Server,Robustness (computer science),Artificial intelligence,Network attack,Machine learning,Vulnerability,Vulnerability detection,Cyberspace | Conference | 0 |
PageRank | References | Authors |
0.34 | 3 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiqiang Wang | 1 | 0 | 4.73 |
| Pingchuan Ma | 2 | 0 | 1.35 |
| Ruming Wang | 3 | 0 | 0.34 |
| Shichun Gao | 4 | 0 | 0.34 |
| Xuying Zhao | 5 | 0 | 0.34 |
| Tao Yang | 6 | 160 | 76.32 |