Name
Abstract | ||
|---|---|---|
Recently cloud computing has emerged the IT world. It eventually promoted the acquisition of resources and services as needed, but it has also instilled fear and user's renunciations. However, Machine learning processing has proven high robustness in solving security flaws and reducing false alarm rates in detecting attacks. This paper, proposes a hybrid system that does not only labels behaviors based on machine learning algorithms using both misuse and anomaly-detection, but also highlights correlations between network relevant features, speeds up the updating of signatures dictionary and upgrades the analysis of user behavior. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-19945-6_6 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Attack-detection,Cloud,IDS,Machine learning,Security,Similarities | False alarm,Computer science,Robustness (computer science),Artificial intelligence,Web application,Hybrid system,Machine learning,Cloud computing | Conference |
Volume | ISSN | Citations |
11407 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amar Meryem | 1 | 0 | 1.01 |
| Mouad Lemoudden | 2 | 0 | 0.34 |
| Bouabid El Ouahidi | 3 | 14 | 6.08 |