基于LSTM和特征生成的网络流量分类

本文提出了一种将特征生成和长短期记忆（long short term memory，LSTM）模型相结合的网络流量分类方法。该方法采用矩阵乘法特征生成方式，分析对比了不同特征生成方法的分类性能。通过实验比较了原数据和特征数据在分类问题上的准确性，并比较了卷积神经网络（convolutional neural network，CNN）和本文方法用于网络流分类的效果。在统计特征时采用核函数，使其可以适应LSTM输入维度，获得更佳的分类效果。对真实网络流数据的实验结果表明，本文方法在细分类中的准确度可达93.9%，而在粗分类任务中可达99.2%，其性能明显优于现有其他分类方法。

Network Traffic Classification Based on LSTM and Feature Generation

This paper proposes a network traffic classification method that combines feature generation and long short term memory (LSTM) model. This method analyzes and compares the classification performances of different feature generation methods using matrix multiplication feature generation method. The accuracy of original data and feature data on the classification problem is tested experimentally, and the results of convolutional neural network (CNN) and the proposed method are compared on network flow classification. The kernel function is used in the statistical feature, so that it can adapt to the LSTM input dimension and obtain better classification results. Experimental results on real network flow data show that the proposed method can achieve 93.9% accuracy in classification, and 99.2% in coarse grained classification task, and this performance is significantly better than that of existing methods.