Name
Abstract | ||
|---|---|---|
Anomalies could be the threats to the network that have ever/never happened. To protect networks against malicious access is always challenging even though it has been studied for a long time. Due to the evolution of network in both new technologies and fast growth of connected devices, network attacks are getting versatile as well. Comparing to the traditional detection approaches, machine learning is a novel and flexible method to detect intrusions in the network, it is applicable to any network structure. In this paper, we introduce the challenges of anomaly detection in the traditional network, as well as in the next generation network, and review the implementation of machine learning in the anomaly detection under different network contexts. The procedure of each machine learning category is explained, as well as the methodologies and advantages are presented. The comparison of using different machine learning models is also summarised. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ACCESS.2021.3126834 | IEEE ACCESS |
Keywords | DocType | Volume |
Anomaly detection, Training, Machine learning, Prediction algorithms, Feature extraction, Predictive models, Security, Machine learning, anomaly detection, network security, software defined network, Internet of Things, cloud network | Journal | 9 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Song Wang | 1 | 0 | 0.34 |
| Juan Fernando Balarezo | 2 | 0 | 0.34 |
| Sithamparanathan Kandeepan | 3 | 6 | 5.85 |
| Akram Al-Hourani | 4 | 0 | 1.01 |
| Karina Gomez Chavez | 5 | 2 | 2.76 |
| Benjamin I. P. Rubinstein | 6 | 486 | 41.87 |