Performance Analysis of Back Propagation Neural Network for Internet Traffic Classification


With rapid increase in internet usage over last few years, the area of internet traffic classification has advanced. it includes applications like www, e-mail, P2P, multimedia, FTP applications, Games etc. Neural Networks are Machine Learning techniques for classiciation. In this paper, Back Propagation Neural Networks (BPNN) are employed for internet traffic classification. This Paper shows that BPNN is an efficient technique for internet traffic classification for reduced feature datasets .

The demand for Internet Traffic Classification that optimizes network performance by solving difficult network management problems for Internet Service Providers (ISPs) and provides quality-of-service (QoS) guarantees has increased substantially in recent years, in part, due to the phenomenal growth of bandwidth-hungry application. Variety of applications running over internet are www, e-mail, p2p, multimedia, FTP applications, interactive services, Games etc which lead to rapid increase in internet traffic. Classification of this internet traffic is necessary in order to solve ISPs network management and monitoring problems such as available bandwidth planning and provisioning, measure of QoS, identification of customer's use of particular application for billing, detection of indicators of denial of service attacks and any severe problem degrading the performance of network etc. Now a day, it is also being utilized by various governmental intelligence agencies from security point of view.

Traditional Internet Traffic Classification Techniques


Internet Traffic classification can be either offline or online. In online classification, analysis is performed while data packets flowing through the network are captured; but in case of offline classification technique, firstly data traces are captured and stored and then analysed later. Traditionally, various internet traffic classification techniques have been based upon direct inspection of packets flowing through the network. These techniques are payload based and port number based packet inspection techniques. In payload based technique , payload of few TCP/IP packets are analysed in order to find type of application which is not possible today because of use of cryptographic techniques used to encrypt data in packet payload and privacy policies of governments which do not allow any unaffiliated third party to inspect each packets payload. In port number based packet inspection technique, well-known port numbers are provided in header of IP packets which are reserved by IANA (Internet Assigned Numbers Authority) for particular applications e.g. port number 80 is reserved for web based applications. Unfortunately, this method also becomes ineffective due to the use of Dynamic port numbers instead of Well-known port numbers for various applications.

Current Trend in Internet Traffic Classification


The diminished effectiveness of traditional port number based and payload based direct packet inspection internet traffic classification techniques motivate us to classify internet traffic into various application categories using Machine Learning (ML) techniques which are based upon supervised and unsupervised learning techniques. Neural Networks which are a massively parallel distributed network consisting of number of information processing units (Neurons) inspired by the way human brain work, also comes under the category of ML techniques.

This paper is based upon use of Back Propagation Neural Network (BPNN) for internet traffic classification. BPNN is a type of supervised multilayer feed forward neural network which is based upon backward flow of error signal between actual output and desired output in order to update weights during training process. In this paper, performance of BPNN is analysed for two different training and testing data sets having different number of features contained in input samples. Performance of this network is evaluated on the basis of accuracy, recall, number of hidden layer neurons and training time . This paper shows that as the back propagation neural network gives good classification accuracy even by reducing the number of features of input samples in training and testing data sets to much extent and it also leads to reduced complexity and reduction in training time of BPNN.

This is introductory part of this research paper. For detailed information, viewers are advised to see the research paper given in the attachment.

Attachment Link: Performance Analysis of Back Propagation Neural Network for Internet Traffic Classification


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