# Identifying Peer-to-Peer Traffic Based on Traffic Characteristics

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3 on each other or upon the existence of the other features a naive Bayes classification algorithm considers all of these properties to independently contribute to the probability that this fruit is a tomato [9]. Same thing is applied for each packet. The naive Bayes consider all the properties of packet and its value to independently contribute to the probability that this packet is P2P packet or Non-P2P packet. C. Module III-Traffic Classification In this, system classifies the traffic and represents the output to user in both textual and graphical form. Input to this module is classified packets. Output of this module is classified traffic. There are two steps in it and these are Traffic Classification and Output. In Traffic Classification step we classify the particular traffic. In Output step system represents the output as two sets namely as P2P and Non-P2P traffic. This step also represents the output in graphical form. IV. MATHEMATICAL MODEL D. System description by means of mathematical formulas As Let, S is a system which is defined in following manner: S = {P, PS, PA, FE, MD, KM, C, CL, NB, O, G, F f1, f2, f3, f4, f5, f6, f7} Fig. 1. State System architecture of proposed system Optimum Thresholds and it is part of it. We apply K-Means algorithm in it. First we consider trainee features of packet then we finds two centroids for packet length that is Centroid 1 for minimum value and Centroid 2 for its maximum value. K-means is one of the simplest unsupervised learning algorithms that solve the widely-known clustering problem. The procedure of K-means algorithm follows a simple and it is easy way to classify a given data set through a certain number of clusters assume as a k clusters, fixed a priori. The main idea is to define k centroids, one for each cluster and these centroids should be placed in a cunning way because of different location causes different result. So, the better choice is to place them as much as possible far away from each other and then, the next step is to take each point belonging to a given data set and associate it to the nearest centroid. In Packet Classification step all packets then classified by using Naive-Bayes classifier algorithm. The input to this step is calculated optimum threshold values, collection of feature set and tracked packet array. We consider the min and max values of packet length of packets in it which is calculated in preprocessing module. We also consider hop limit and port number features. The naive Bayes algorithm is a simple probabilistic classification algorithm. In simple terms, a naive Bayes classifier assumes that the presence or absence of a particular feature of a class is not related to the presence or absence of any other feature, given the class variable. For example, a fruit may be considered to be a tomato if it is red, round, and about 3 in diameter. Even if these features depend Where, P = Set of Packets PS = Packet Sniffer PA = Set of Analyzed Packets FE = Feature Extraction MD = Manage Database KM = K-Means Algorithm C = Set of Centroids CL = Set of Clusters NB = Nave-Bayes Algorithm O = Set of Output G = Set of Graphs SET THEORY: P = {P0, P1,..., Pn} PA = {PA0, PA1,..., PAn} C = {C0, C1,..., Cn} CL = {CL0, CL1,..., CLn} O = {P2P, Non P2P} G = {G0, G1,..., Gn} ISBN:

4 TABLE I. MAPPING TABLE Sr. No. Function Description 1 f1(p) P Tracking traffic by using Packet Sniffer. 2 f2(p) PA Analyze Packet using Packet Analyzer. 3 f3(pa) D For Updating Dataset of the system. 4 f4(d, FE) PE Packet of Particular Feature. 5 f5(pe, C, CL) CL To Find cluster. 6 F6(CL) C To get Classified Traffic. 7 f7(c) O, G To represent Output as Set and Graph. INPUT: P = {P0, P1,..., Pn} OUTPUT: O = {P2P, Non P2P} G = {G0, G1,..., Gn} FUNCTIONS: f1 = For Packet Sniffer to capture network traffic f2 = For Packet Analyzing f3 = For Updating Dataset f4 = For Feature Extraction f5 = For Finding Clusters f6 = For getting Classified Traffic f7 = Output as Graph E. Function mapping table Above functions can be mapped onto the elements of the set. It is shown in table I. F. State Transaction Diagram The basic idea of this diagram is to define a machine that has finite states. It represents the states of the system. Following figure represents the state transaction diagram of the proposed system. Fig. 2. State transition diagram of system V. ANALYSIS OF RESULT AND ALGORITHM PERFORMANCE In campus network it is hard to determine the P2P host. Even we could determine the nodes which use P2P but it is hard to determine how much of traffic is generated by P2P and non-p2p applications. So we conduct the experiments in the laboratory. In that experiment we used some sets of experimental machines which is used for generating enough amount of P2P traffic. It is used for testing robustness and detection accuracy rate of the earlier and proposed methods. These experimental machines generate different types of P2P and Non-P2P traffic during different period of time. We set threshold value and we analyzed network for 1 week. Results of the experiments are shown in Table II. As we are using K-means for determining min and max threshold values of properties of packet, it makes our analysis and proposed scheme more accurate. The time complexity of proposed method is less than the previous method as we are using Naïve-Bayes classification algorithm. Although the proposed method in this paper still having some weaknesses and false detection rate but they are acceptable. TABLE II. EFFRCTIVENESS COMPARISON BETWEEN EARLIER TRAFFIC CHARACTERISTICS BASED METHOD AND METHOD IN THE PAPER Application Earlier traffic Earlier traffic Expected Expected characteristics characteristics result of result of based method based method negative false rate negative rate false rate rate in the in the paper paper BT 5.34% 2.86% 2.3% 1.86% emule 5.82% 3.23% 2.82% 1.23% Pplive 4.27% 4.26% 2.29% 2.63% Kugoo 4.63% 3.25% 2.56% 2.14% Non-P2P 4.26% 4.84% 2.18% 2.44% VI. CONCLUSION The traffic identification is very relevant for the study of network traffic control, traffic congestion, resource utilization and it also has an economic importance. With the P2P technology continues to progress, P2P client has experienced the phase of using fixed-port, random port, encrypted message and the tunnel mode. It is hard for people to identify packets of P2P protocols. Some of the traditional identification ISBN:

5 methods like the Port-based identification method, Payloadbased identification method and Feature-based identification are not very effective. Traffic identification technologies such as traffic identification by using network characteristics have been considered for solving the problem with improved accuracy rate and improved algorithm efficiency. The previous work of P2P traffic identification based on traffic characteristics, it gives the high rate of false detection and high rate of negative detection and it is not efficient when we consider time as a factor. So we designed a system in such a way that it minimizes the rate of false detection and the rate of negative detection and classifies the traffic in efficient manner. The presented method in this paper can able to detect P2P traffics with dynamic ports and encrypted transmission, and the efficiency of the implementation of the algorithm is also improved to some extent. These techniques are useful for increasing the performance of the network. This system can easily extend for working with other regions of the network in order to solve the problems in it. ACKNOWLEDGMENT We are thankful to Mr. S. D. Baber and Mr. P. J. Pandit, for the encouragement and the support they have extended to us for completing this paper. We are also thankful to the Mrs. S. R. Patil and Mr. Sanjay Dangat for their support to make this paper as good as it is. We are also thankful to our family members and friends for patience and encouragement. REFERENCES [1] Yu-shui Geng, Tao Han and Xue-song Jiang The research of P2P traffic identification technology. ( /09, 2009, IEEE). [2] Jingyu Wang, Jiyuan Zhang, Yuesheng Tan Research of P2P Traffic Identification Based on Traffic Characteristics ( /11, 2011, IEEE). [3] Jian Feng Research on the Technology of Peer-to-Peer Traffic Classification. ( /10,2010, International Symposium on Computer, Communication, Control and Automation, IEEE). [4] S. Subhabrata, S. Oliver, D. Wang, Accurate, scalable in-network identification of p2p traffic using application signatures, Proc. The 13 th international conference on World Wide Web, ACM Press, Oct. 2004, pp , doi: / [5] Ke Xu, Ming Zhang, Mingjiang Ye, Dah Ming Chiu, Jianping Wu Identify P2P traffic by inspecting data transfer behavior. ( , Elsevier B.V., 2010, Computer Communications). [6] CHENG Wei-qing, GONG Jian, DING Wei Identifying file-sharing P2P traffic based on traffic characteristics. (15(4): , December 2008, paper number: , Sciencedirect). [7] Haiming Jiang, Jianying Zhang, Qingqing Wang, P2P traffic detection and analysis, J. Computer Technology and Development, 2008, 18 (7): [8] Chao Wen, Xuefeng ZHENG, The study of P2P protocol identification method based on the traffic analysis J. Micro Computer Applications, 2007 (7): [9] Mrs.G.Subbalakshmi, Mr. K. Ramesh, Mr. M. Chinna Rao Decision Support in Heart Disease Prediction System using Naive Bayes (ISSN : , Vol. 2 No. 2 Apr-May 2011, Indian Journal of Computer Science and Engineering (IJCSE) ). ISBN:

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