基于多特征融合的移动流量应用识别

Mobile Traffic Application Recognition Based on Multi-Feature Fusion

移动应用识别是移动网络安全与管理研究领域的一项关键技术。针对移动应用更新后人工提取特征失效及特征提取不充分等问题,文章提出一种基于流量的移动应用识别模型MAITSF。该模型采用多通道并行架构,利用卷积神经网络(Convolutional Neural Network,CNN)提取移动应用流量的空间特征,使用长短时记忆(Long Short-Term Memory,LSTM)网络提取移动应用流量的时间特征,并融合各通道提取的特征。在此基础上,引入一个通道注意力模块对每个通道赋予不同权重,使模型能够集中关注神经网络提取的关键特征,增强流量特征的表征能力。文章在公开数据集(CIC-AAGM2017)和实验室采集的实际数据集上进行对比实验,实验结果表明,MAITSF在两个数据集上的分类准确率均达98%,相较于现有典型模型提高了4%以上。

Mobile application recognition is a key technology in the research field of mobile network security and management.Aiming at the failure of manual feature extraction after mobile applications update and insufficient feature extraction,this paper proposed a new traffic-based mobile application recognition model called MAITSF.The model adopted a multi-channel parallel architecture.In this model,the convolutional neural network(CNN)was used to extract the spatial characteristics of mobile application traffic,and the long short-term memory(LSTM)network was used to extract the temporal characteristics,and the features extracted from each channel were fused.On this basis,a channel attention module was introduced to allocate a series of weight parameters,so that the model can focus more on the key features extracted by the neural network,and enhance the ability of traffic characteristics characterization.In this paper,comparative experiments were carried out on the public dataset(CIC-AAGM2017)and the actual dataset collected in the laboratory.The experimental results show that the classification accuracy of MAITSF on the above two datasets reached 98%,which is more than 4%higher than the existing typical models.