A novel internet traffic identification approach using wavelet packet decomposition and neural network

Abstract

Internet traffic classification plays an important role in network management, and many approaches have been proposed to classify different kinds of internet traffics. A novel approach was proposed to classify network applications by optimized back-propagation (BP) neural network. Particle swarm optimization (PSO) algorithm was used to optimize the BP neural network. And in order to increase the identification performance, wavelet packet decomposition (WPD) was used to extract several hidden features from the time-frequency information of network traffic. The experimental results show that the average classification accuracy of various network applications can reach 97%. Moreover, this approach optimized by BP neural network takes 50% of the training time compared with the traditional neural network.

Keywords

neural network / particle swarm optimization / statistical characteristic / traffic identification / wavelet packet decomposition

References

[1] AzzounaN. B., GuilleminF.. Impact of peer-to-peer applications on wide area network traffic [C]. Proc of IEEE Global Telecommunications Conference, 2004TexasIEEE1544-1548

[2] KimJ. T., ParkH. K., PaikE. H.. Security issues in peer-to-peer systems [C]. The 7th International Conference on Advanced Communications Technology, 2005Phoenix ParkIEEE1059-1063

[3] SenS., WangJ.. Analyzing peer-to-peer traffic across large networks [J]. IEEE Trans on Networking, 2004, 2(2): 219-231

[4] SenS., SpatscheckO., WangD.-mei.. Accurate, scalable in-network identification of P2P traffic using application signatures [C]. Proceedings of ACM www′04, 2004New YorkACM512-521

[5] ErmanJ., MahantiA., ArlittM., CohenI., WilliamsonC.. Semi-supervised network traffic classification [C]. Proceedings of the ACM SIGMETRICS, 2007New YorkACM369-370

[6] ZanderS., NguyenT., ArmitageG.. Self-learning IP traffic classification based on statistical flow characteristics [J]. Passive and Active Network Measurement, 2005, 3431: 325-328

[7] MOORE A W, ZUEV D. Internet traffic classification using bayesian analysis techniques [C]// Proceedings of the ACM SIGMETRICS. 2005: 50–60.

[8] MooreA. W., PapagiannakiK.. Towards the accurate identification of network applications [C]. Passive and Active Measurements Workshop, 2005Boston, MA, USASpringer Press41-54

[9] HaffnerP., SenS., SpatscheckO., WangD.-mei.. ACAS: automated construction of application signatures [C]. Proceedings of the 2005 ACM SIGCOMM Workshop on Mining Network Data, 2005New YorkACM192-202

[10] KaragiannisT., BroidoA., FaloutsosM., ClaffyK.. Transport layer identification of P2P traffic [C]. Proceedings of the 4th ACM SIGCOMM conference on Internet Measurement, 2004Sicily, USAACM121-134

[11] XU Ke, ZHANG Ming, YE Ming-jiang, CHIU Dah-ming, WU Jian-ping. Identify P2P traffic by inspecting data transfer behavior [J]. Computer Communications, 2010 (33): 1141–1150.

[12] FilhoR. H., CarmoM. F. F. D., MaiaJ. E. B., JuniorG. P. S.. An Internet traffic classification methodology based on statistical discriminators [C]. Network Operations and Management Symposium, 2008SalvadorIEEE907-910

[13] WilliamsN., ZanderS., ArmitageG.. A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification [J]. SIGCOMM Computer Communication Review, 2006, 36(5): 5-16

[14] LU X, DUAN H, LI X. Identification of P2P traffic based on the content redistribution characteristic [J]. Communications and Information Technologies, 2007: 596–601.

[15] ZanderS., NguyenT., ArmitageG.. Automated traffic classification and application identification using machine learning [C]. Proceedings of the IEEE Conference on Local Computer Networks 30th Anniversary, 2005Sydney, NSWIEEE Press250-257

[16] EsteA., GringoliF., SalgarelliL.. Support vector machines for TCP traffic classification [J]. Computer Networks, 2009, 53(14): 2476-2490

[17] LiZ., YuanR.-x., GuanX.-hong.. Accurate classification of the internet traffic based on the SVM method [C]. IEEE International Conference on Communications, 2007GlasgowIEEE Press1373-1378

[18] RaahemiB., ZhongW.-c., LiuJing.. Peer-to-peer traffic identification by mining IP layer data streams using concept-adapting very fast decision tree [C]. Proc of the 20th IEEE International Conference on Tools with Artificial Intelligence, 2008Dayton, USAIEEE Press525-532

[19] AuldT., MooreA. W., GullS. F.. Bayesian neural networks for internet traffic classification [J]. IEEE Transaction on Neural Networks, 2007, 18(1): 223-239

[20] SunR.-y., YangB., PengL.-z., ChenZ.-x., ZhangL., JingShan.. Traffic classification using probabilistic neural networks [C]. Sixth International Conference on Natural Computation (ICNC), 2010YantaiIEEE Press1914-1919

[21] CalladoA., KelnerJ., SadokD., KamienskiC. A., FernandesS.. Better network traffic identification through the independent combination of techniques [J]. Journal of Network and Computer Applications, 2010, 33(4): 433-446

[22] EkiciS., YildirimS., PoyrazM.. Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition [J]. Expert Systems with Applications, 2007, 34(4): 2937-2944

[23] WuT., YanG.-z., YangB.-h., SunHong.. EEG feature extraction based on wavelet packet decomposition for brain computer interface [J]. Measurement, 2007, 41(6): 618-625

[24] SedkiA., OuazarD., MazoudiE. E.. Evolving neural network using real coded genetic algorithm for daily rainfall-runoff forecasting [J]. Expert Systems with Applications, 2009, 36(3): 4523-4527

[25] LiuZ.-j., LiuA.-x., WangC.-y., NiuZheng.. Evolving neural network using read coded genetic algorithm^(GA) for multispectral image classification [J]. Future Generation Computer Systems, 2004, 20(7): 1119-1129

[26] ZhangJ.-r., ZhangJ., LokT.-m., LyuM. R.. A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training [J]. Applied Mathematics and Computation, 2007, 185(2): 1026-1037

[27] KennedyJ., EberhartR.. Particle swarm optimization [C]. Proceeding of IEEE International Conference on Neural Networks, 1995PerthIEEE Press1942-1947

[28] LiT.-s., HsuC.-ming.. Parameter optimization of sub-35 nm contact-hole fabrication using particle swarm optimization approach [J]. Expert Systems with Applications, 2010, 37(1): 878-885

[29] LinC.-j., WangJ.-g., LeeC.-yung.. Pattern recognition using neural-fuzzy networks based on improved particle swarm optimization [J]. Expert Systems with Applications, 2009, 36(3): 5402-5410

[30] YuS.-w., ZhuK.-j., DiaoF.-qin.. A dynamic all parameters adaptive BP neural networks model and its application on oil reservoir prediction [J]. Applied Mathematics and Computation, 2008, 195(1): 66-75