Internet traffic identification algorithm based on adaptive BP neural network

被引：2
作者：
Tan, Jun [1 ]
Chen, Xing-Shu [1 ]
Du, Min [1 ]
Zhu, Kai [1 ]
机构：
[1] Network and Trusted Computing Institute, Sichuan University
来源：
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2012年 / 41卷 / 04期
关键词：
Adaptive algorithm; Neural networks; Particle swarm optimization; Statistical characteristic; Traffic identification;
D O I：
10.3969/j.issn.1001-0548.2012.04.020
中图分类号：
学科分类号：
摘要：
Internet traffic identification is currently an important challenge for network management. Traditional approaches focus on identifying TCP flows and cannot accurately classify emerging network applications. In this paper, a new approach based on adaptive back-propagation (BP) neural network is proposed to identify both TCP and UDP traffic flows. This approach uses the dual particle swarm optimization (PSO) algorithm to optimize the BP neural network. The experimental results show that the proposed approach can classify both TCP and UDP traffic flows at a high rate and can reduce the training time and adjust the number of hidden layer nodes of BP neural network adaptively.
引用
收藏
页码：580 / 585
页数：5
相关论文
共 14 条
  • [1] Azzouna N.B., Guillemin F., Impact of peer-to-peer applications on wide area network traffic: An experimental approach, Proc of IEEE Global Telecommunications Conference, pp. 1544-1548, (2004)
  • [2] Jung-Tae K., Hae-Kyeong P., Eui-Hyun P., Security issues in peer-to-peer systems, The 7th International Conference on Advanced Communications Technology, pp. 1059-1063, (2005)
  • [3] Sen S., Wang J., Analyzing peer-to-peer traffic across large networks, IEEE Trans on Networking, 2, 2, pp. 219-232, (2004)
  • [4] Sen S., Spatscheck O., Wang D., Accurate, scalable in-network identification of P2P traffic using application signatures, Proceedings of ACM WWW'04, pp. 512-521, (2004)
  • [5] Karagiannis T., Broido A., Transport layer identification of P2P traffic, Proceedings of the 4th ACM SIGCOMM Conference on Internet Measurement, pp. 121-134, (2004)
  • [6] Este A., Gringoli F., Salgarelli L., Support vector machines for TCP traffic classification, Computer Networks, 53, 14, pp. 2476-2490, (2009)
  • [7] Li Z., Yuan R.-X., Guan X.-H., Accurate classification of the internet traffic based on the SVM Method, IEEE International Conference on Communications, pp. 1373-1378, (2007)
  • [8] Raahemi B., Zhong W.-C., Liu J., Peer-to-peer traffic identification by mining IP layer data streams using concept-adapting very fast decision tree, Proc of the 20th IEEE International Conference on Tools with Artificial Intelligence, pp. 525-532, (2008)
  • [9] Auld T., Moore A.W., Gull S.F., Bayesian neural networks for internet traffic classification, IEEE Transaction on Neural Networks, 18, 1, pp. 223-239, (2007)
  • [10] Chen H.-W., Hu Z.-B., Ye Z.-W., Et al., Research of P2P traffic identification based on neural network, International Symposium on Computer Network and Multimedia Technology, pp. 1-4, (2009)
12