An Improved Stacked Auto-Encoder for Network Traffic Flow Classification
An Improved Stacked Auto-Encoder for Network Traffic Flow Classification
复制标题
一种改进的网络流量分类堆叠式自动编码器
DOI：
10.1109/mnet.2018.1800078
复制
发表时间：
2018-11-01
期刊：
影响因子：
9.3
通讯作者：
Deen, M. Jamal
中科院分区：
文献类型：
--
作者：
Li, Peng;Chen, Zhikui;Deen, M. Jamal
网络流分类在各种网络应用中起着非常重要的作用，是网络流量控制的一项基础性工作。然而，多源网络应用和弹性网络架构的创新，以及网络流量的高容量、高速度、高多样性和高准确性，对网络流量的准确分类提出了前所未有的挑战。本文提出了一种改进的堆叠式自动编码器，通过堆叠几个基本的贝叶斯自动编码器来学习多源网络流上的复杂关系。具体来说，为了对网络流中包含的不确定性进行建模，使用无监督学习策略在对象上训练贝叶斯自动编码器。此外，堆叠式自动编码器通过使用监督学习策略的反向传播算法来训练，以捕获网络流上的复杂关系。最后，为了评估改进模型的性能，在两个具有代表性的网络流数据集，即MAWI和DARPA 99的基础上合成数据集进行了广泛的实验。实验结果表明，改进的层叠式自动编码器在分类精度上优于传统的层叠式自动编码器。
Network flow classification plays a very important role in various network applications and is a fundamental task in network flow control. However, the innovations in the multi-source network application and the elastic network architecture with the network flows of high volume, velocity, variety, and veracity pose unprecedented challenges on accurate network flow classification. In this article, an improved stacked auto-encoder is proposed to learn the complex relationships over the multi-source network flows by stacking several basic Bayesian auto-encoders. Specifically, to model the uncertainty contained in the network flows, the Bayesian auto-encoder is trained on the objects using the unsupervised learning strategy. Furthermore, the stacked auto-encoder is trained by the back-propagation algorithm using the supervised learning strategy to capture the complex relationships over the network flows. Finally, to assess the performance of the improved model, extensive experiments are conducted on two synthetic datasets based on the representative network flow datasets, that is, MAWI and DARPA 99. The results demonstrate that the improved stacked auto-encoder outperforms the traditional one in terms of classification accuracy.