信息网络安全 ›› 2024, Vol. 24 ›› Issue (1): 60-68.doi: 10.3969/j.issn.1671-1122.2024.01.006
赵佳1,2, 杨博凯1,2(), 饶欣宇1,2, 郭雅婷1,2
收稿日期:
2023-11-14
出版日期:
2024-01-10
发布日期:
2024-01-24
通讯作者:
杨博凯
E-mail:23120488@bjtu.edu.cn
作者简介:
赵佳(1980—),女,内蒙古,副教授,博士,CCF会员,主要研究方向为密码学、隐私保护|杨博凯(2000—),男,黑龙江,硕士研究生,主要研究方向为联邦学习|饶欣宇(1999—),女,四川,硕士研究生,主要研究方向为联邦学习、密码学|郭雅婷(1998—),女,山东,硕士研究生,主要研究方向为联邦学习、密码学
基金资助:
ZHAO Jia1,2, YANG Bokai1,2(), RAO Xinyu1,2, GUO Yating1,2
Received:
2023-11-14
Online:
2024-01-10
Published:
2024-01-24
Contact:
YANG Bokai
E-mail:23120488@bjtu.edu.cn
摘要:
Tor网络作为第二代匿名互联网通信系统,常被网络罪犯用于进行网络攻击和欺诈等恶意活动,给网络安全带来了严重的威胁和挑战。为解决该问题,文章提出一种基于联邦学习的Tor流量检测方法。目前Tor流量检测以单主机检测为主,存在效率低和无法实现数据共享的问题,文章采用联邦学习技术和DP-SGD算法确保各参与方在保护用户隐私的前提下构建全局模型,解决数据孤岛问题。实验证明,该模型在保障用户数据隐私的同时,具有92%的整体准确率、90%的准确率和92%的召回率。文章通过对比实验进一步验证了模型在隐私保护和分类效果上的优越性。
中图分类号:
赵佳, 杨博凯, 饶欣宇, 郭雅婷. 基于联邦学习的Tor流量检测算法设计与实现[J]. 信息网络安全, 2024, 24(1): 60-68.
ZHAO Jia, YANG Bokai, RAO Xinyu, GUO Yating. Design and Implementation of Tor Traffic Detection Algorithm Based on Federated Learning[J]. Netinfo Security, 2024, 24(1): 60-68.
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