Ambit lick Solutions
Mail Id: Ambitlick@gmail.com , Ambitlicksolutions@gmail.Com
Network Traffic Classification Using Corr...
Ambit lick Solutions
Mail Id: Ambitlick@gmail.com , Ambitlicksolutions@gmail.Com
classification performance can be improve...
Upcoming SlideShare
Loading in...5
×

Network traffic classification using correlation information

629

Published on


Bulk Projects For sale

IEEE 2009-10-11-12-13 PAPERS AVILABLE.

We are providing low cost project for final year student projects.

Solved 2010 -2011 -2012 - 2013 IEEE in all the domain

Mobile : 8940956123

E-Mail : ambitlick@gmail.com,

INNOVATIVE TITLES ARE ALSO WELLCOME TO DO WITH US


For All BE/BTech, ME/MTech, MSC/MCA/MS , and diplamo graduates

PROJECT SUPPORTS & DELIVERABLES

•Project Abstract
•IEEE Paper
•PPT / Review Details
•Project Report
•Working Procedure in Video
•Screen Shots
•Materials & Books in CD
•Project Certification

Published in: Education
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
629
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
8
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Network traffic classification using correlation information

  1. 1. Ambit lick Solutions Mail Id: Ambitlick@gmail.com , Ambitlicksolutions@gmail.Com Network Traffic Classification Using Correlation Information Traffic classification has wide applications in network management, from security monitoring to quality of service measurements. Recent research tends to apply machine learning techniques to flow statistical feature based classification methods. The nearest neighbor (NN)-based method has exhibited superior classification performance. It also has several important advantages, such as no requirements of training procedure, no risk of overfitting of parameters, and naturally being able to handle a huge number of classes. However, the performance of NN classifier can be severely affected if the size of training data is small. In this paper, we propose a novel nonparametric approach for traffic classification, which can improve the classification performance effectively by incorporating correlated information into the classification process. We analyze the new classification approach and its performance benefit from both theoretical and empirical perspectives. A large number of experiments are carried out on two real-world traffic data sets to validate the proposed approach. The results show the traffic
  2. 2. Ambit lick Solutions Mail Id: Ambitlick@gmail.com , Ambitlicksolutions@gmail.Com classification performance can be improved significantly even under the extreme difficult circumstance of very few training samples.

×