1. A PATTERN MATCHING APPROACH
TO CLASSIFICATION OF INTERNET TRAFFIC
Submitted By
P.SAKTHIVEL(Reg.No. 2010PR197)
Under the Guidance of
C.SENTHILKUMAR M.Sc.,M.Phil.,
Associate Professor, Dept. of Computer Science,
Erode Arts & Science College,Erode-638009.

2. Objective of Study
Different types Online and Offline Tools were developed to
understanding the character of internet traffic types.
New approaches are to identify proposed traffic modeling.
Data sets were collected from different organizations and
different types of networks.

3. Abstract
The past network traffic used self-similarity particularly
linear predication based traffic model.
Auto regressive / ARMA models have been used monitor
the Network traffic.
The traffic type can be characterized by Packet length,
Packet inter arrival time, Connection direction, Connection
Packet count and byte count.

4. The Proposed model used mixture distribution under
simple K-means clustering based approach
To facilitate the traffic analysis, developed several tools.

5. Literature Review on traffic modeling
Existing Model:
Linear Predication based traffic model
LP based network traffic uses the past history of network
traffic to predict the future.
This model have a minimal number of parameters and
overhead for modeling.
It identify the optimal value of LP parameters for network
traffic

6. The LP model can predict the occurrence of network
congestion.
LP model by itself does not lead to the source of
congestion.

7. Need for a New model
LP based traffic model can predict a network fault but
cannot find the source of the network fault.
A new model is required to overcome the limitation of
recent LP-based network traffic model.

8. Tools Developed and Data Sets Collected
 Offline Tool
 Online Tool
 WANMon Tool
 Control Tool
Data Sets
 TeNeT-WAN-2005
 TeNeT-LAN-2005
 BHEL-WAN-2005

9. UNIVARIATE DISTRIBUTION MODELING
Fitting Univariate Distributions to Internet Tra_c
Types:
Analysis can be divided into the following steps:
 Identifying the shape of the density
 Estimating the unknown parameters
 Performing a test of fit

10.  Identifying the Shape of the Density
For every member of this system, the probability density
fx(x) satises the differential equation.
 Estimating the Unknown Parameters
The general form of density function for the Type I family
of distributions is given below:

11.  Performing a Test of Fit
Under the condition that there are k intervals of values of x
giving nonzero expected frequencies. The parameters in
equation (4.17) are as follows:
• nfe(x) the expected frequencies,
• foi the observed frequencies,
• n the total number of samples.

12. MIXTURE DISTRIBUTION MODELING
K-Means Clustering Based Approach
 Training:
A series of K-means clustering models are trained to
model the traffic types from the available training data.
 Testing:
Minimum Distance Computation (MDC) identifies the
traffic type by calculating the distance of an unknown
traffic type to these models

13. FLOW CHART OF K-MEANS CLUSTERING APPROACH

18. PERFORMANCE EVALUATION
 List of Experiments

19.  Effect of training data on model performance

20.  Effect of number of models

21.  Effect of amount of test data

22.  Use of Different Features

23. CONCLUSION
 Easily access the internet to the user.
 High bandwidth.
 Variety of off and on line tools are used.
 Easily identify the traffic model.

24. Thank You