http://iet.metastore.ingenta.com
1887

## A hybrid clustering-classification for accurate and efficient network classification

• Author(s):
• DOI:

$16.00 (plus tax if applicable) ##### Buy Knowledge Pack 10 chapters for$120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:

Network Classification for Traffic Management: Anomaly detection, feature selection, clustering and classification — Recommend this title to your library

## Thank you

The traffic classification is the foundation for many network activities, such as quality of service (QoS), security monitoring, lawful interception, and intrusion detection system (IDS). A recent statistics-based method to address the unsatisfactory results of traditional port-based and payload-based methods has attracted attention. However, the presence of non-informative attributes and noise instances degrade the performance of this method. Thus, to address this problem, in this chapter, a hybrid clustering-classification method (called CluClas) is described to improve the accuracy and efficiency of network traffic classification by selecting informative attributes and representative instances. An extensive empirical study on four traffic data sets shows the effectiveness of the CluClas method.

Chapter Contents:

• 10.1 Introduction
• 10.2 Existing solutions
• 10.3 CluClas—a hybrid clustering and classification method
• 10.3.1 Discarding irrelevant and redundant attributes
• 10.3.2 Identifying representative instances in CluClas
• 10.3.3 The CluClas learning process
• 10.3.4 Classification/Prediction process in CluClas method
• 10.4 Experimental evaluation
• 10.4.1 Experimental setting
• 10.4.2 Traffic data sets
• 10.4.3 Evaluation metrics
• 10.4.4 Results and discussion
• 10.5 Conclusion

Preview this chapter:

A hybrid clustering-classification for accurate and efficient network classification, Page 1 of 2

| /docserver/preview/fulltext/books/pc/pbpc032e/PBPC032E_ch10-1.gif /docserver/preview/fulltext/books/pc/pbpc032e/PBPC032E_ch10-2.gif

### Related content

content/books/10.1049/pbpc032e_ch10
pub_keyword,iet_inspecKeyword,pub_concept
6
6
This is a required field