Name
Title | ||
|---|---|---|
Machine Learning-Based EDoS Attack Detection Technique Using Execution Trace Analysis. |
Abstract | ||
|---|---|---|
One of the most important benefits of using cloud computing is the benefit of on-demand services. Accordingly, the method of payment in the cloud environment is pay per use. This feature results in a new kind of DDOS attack called Economic Denial of Sustainability (EDoS), in which the customer pays extra to the cloud provider as a result of the attack. Similar to other DDoS attacks, EDoS attacks are divided into different types, such as (1) bandwidth-consuming attacks, (2) attacks that target specific applications, and 3) connection-layer exhaustion attacks. In this work, we propose a novel framework to detect different types of EDoS attacks by designing a profile that learns from and classifies the normal and abnormal behaviors. In this framework, the extra demanding resources are only allocated to VMs that are detected to be in a normal situation and therefore prevent the cloud environment from attack and resource misuse propagation. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s41635-018-0061-2 | Journal of Hardware and Systems Security |
Keywords | DocType | Volume |
DDoS attacks, EDoS attacks, Cloud computing, Machine learning, Detection | Journal | 3 |
Issue | ISSN | Citations |
2 | 2509-3428 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hossein Abbasi | 1 | 0 | 0.34 |
| Naser Ezzati-Jivan | 2 | 53 | 7.62 |
| Martine Bellaïche | 3 | 75 | 9.68 |
| chamseddine talhi | 4 | 192 | 23.98 |
| Michel R. Dagenais | 5 | 4 | 2.18 |