Name
Title | ||
|---|---|---|
Hypervisor-based cloud intrusion detection through online multivariate statistical change tracking |
Abstract | ||
|---|---|---|
Cloud computing is facing a multidimensional and rapidly evolving threat landscape, making intrusion detection more challenging. This paper introduces a new hypervisor-based cloud intrusion detection system (IDS) that uses online multivariate statistical change analysis to detect anomalous network behaviors. As a departure from the conventional monolithic network IDS feature model, we leverage the fact that a hypervisor consists of a collection of instances, to introduce an instance-oriented feature model that exploits the individual and correlated behaviors of instances to improve the detection capability. The proposed approach is evaluated by collecting and using a new cloud intrusion dataset that includes a wide variety of attack vectors. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.cose.2019.101646 | Computers & Security |
Keywords | Field | DocType |
Cloud computing,Cloud security monitoring,Hypervisor-based intrusion detection,Anomaly detection,Change detection,Multistage attacks | Data mining,Computer science,Computer security,Multivariate statistics,Change tracking,Hypervisor,Exploit,Feature model,Change analysis,Intrusion detection system,Cloud computing | Journal |
Volume | ISSN | Citations |
88 | 0167-4048 | 1 |
PageRank | References | Authors |
0.36 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdulaziz Aldribi | 1 | 1 | 0.36 |
| Issa Traore | 2 | 306 | 32.31 |
| Belaid Moa | 3 | 5 | 5.53 |
| Onyekachi Nwamuo | 4 | 1 | 0.36 |