电子科技大学学报2012，Vol.41Issue(4)：580-585,6.DOI:10.3969/j.issn.1001-0548.2012.04.020
基于自适应BP神经网络的网络流量识别算法
Internet Traffic Identification Algorithm Based on Adaptive BP Neural Network
摘要
Abstract
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.关键词
自适应算法/神经网络/粒子群优化/统计特征/流量识别Key words
adaptive algorithm/ neural networks/ particle swarm optimization/ statistical characteristic/ traffic identification分类
信息技术与安全科学引用本文复制引用
谭骏,陈兴蜀,杜敏,朱锴..基于自适应BP神经网络的网络流量识别算法[J].电子科技大学学报,2012,41(4):580-585,6.基金项目
国家973项目(2007CB311106) （2007CB311106）
国防重点实验室基金(NEUL20090101) （NEUL20090101）
