Online Incremental Learning for High Bandwidth Network Traffic Classification
Data stream mining techniques are able to classify evolving data streams such as network traffic in the presence of concept drift. In order to classify high bandwidth network traffic in real-time, data stream mining classifiers need to be implemented on reconfigurable high throughput platform, such...
Saved in:
Main Authors: | H. R. Loo, S. B. Joseph, M. N. Marsono |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2016-01-01
|
Series: | Applied Computational Intelligence and Soft Computing |
Online Access: | http://dx.doi.org/10.1155/2016/1465810 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Method based on contrastive incremental learning for fine-grained malicious traffic classification
by: Yifeng WANG, et al.
Published: (2023-03-01) -
Improving bandwidth utilization by compressing small-payload traffic for vehicular networks
by: Jianan Sun, et al.
Published: (2019-04-01) -
Sampling method for IDS in high bandwidth network
by: NING Zhuo1, et al.
Published: (2009-01-01) -
Research on network traffic classification based on machine learning and deep learning
by: Yue GU, et al.
Published: (2021-03-01) -
Incremental Data Stream Classification with Adaptive Multi-Task Multi-View Learning
by: Jun Wang, et al.
Published: (2024-03-01)