基于表征学习的网络游戏流量识别

Online Game Flow Identification Based on Representation Learning

进行基于表征学习的网络游戏流量识别研究.首先,由于流量识别领域公开数据集中缺乏游戏流量,采集各类游戏流量,并建立各种游戏与进程端口的映射关系,基于该映射关系从采集的流量中过滤游戏流量,扩展公开数据集;利用深度学习中的表征学习模型,对经过预处理的原始端到端游戏流量自动进行特征学习和特征选择;最后用分类器进行游戏类别识别.通过构建特征空间由卷积神经网络自学习原始信息的特征,成功避免传统机器学习算法中流量数据集的二次处理导致的信息丢失以及流量分类模型对特征选择的依赖.实验结果表明,相比于原数据集的分类效果,扩充后的数据集在神经网络模型上的分类准确率提高了5%,游戏流量识别准确率达到92%,识别性能明显提升.

This study explores the online game flow identification based on representation learning.First of all,due to the lack of game flow in the public data set in the field of flow identification,various types of game flow are collected,and a mapping relationship between various games and process ports is established.Depending on the mapping relationship,the game flow is filtered from the collected flow to expand the public data set.Then the representation learning model in deep learning is used to automatically perform feature learning and feature selection on the pre-processed original end-to-end game flow.Finally,the game category is identified by a classifier.The convolutional neural network self-learns the features of the original information via the construction of feature space,successfully avoiding the loss of information caused by the secondary processing of the flow data set in the traditional machine learning algorithm and the dependence of the flow classification model on feature selection.The experimental results show that,compared with the classification effect of the original data set,the expanded data set has a classification accuracy improved by 5%on the neural network model.The accuracy of game flow identification reaches 92%,and the identification performance is significantly improved.