信息工程大学学报2024，Vol.25Issue(4)：459-465,7.DOI:10.3969/j.issn.1671-0673.2024.04.014
改进移动加密流量分类的方法——数据质量分数
Method to Improve Mobile Encryption Traffic Classification——Data Quality Score
摘要
Abstract
The rapid development of mobile internet has led to a surge in demand for classifying en-crypted mobile traffic.Deep learning classification methods rely on data features,but there are differ-ences in the feature quantities of different data,and evenly distributing weights may decrease perfor-mance.To address this issue,we propose a method—Data Quality Score(DQS)to differentiate data and use different weights in the loss function to reduce the interference of low-quality data on model parameters,while enhancing the effect of high-quality data.The effectiveness of this method is verified through experiments on the Mirage-2019 dataset.We first conduct statistical analysis on this dataset to determine feature selection.Then,we build classification models with different neural network struc-tures for experiments and compare their performance with and without DQS method.Results of 5-fold cross-validation indicate that after incorporating the DQS method,the classification performance of dif-ferent network models has been improved without apparent increase in training time.关键词
深度学习/加密流量分类/移动应用程序/数据质量分数/Mirage-2019数据集/损失函数/5折交叉验证Key words
deep learning/encrypted traffic classification/mobile apps/data quality score/Mirage-2019 dataset/loss function/5-fold cross-validation分类
信息技术与安全科学引用本文复制引用
程槟,魏福山,顾纯祥..改进移动加密流量分类的方法——数据质量分数[J].信息工程大学学报,2024,25(4):459-465,7.基金项目
国家自然科学基金(61772548) （61772548）
河南省优秀青年基金(222300420099) （222300420099）
