MTL-DoHTA:基于多任务学习的DNS在HTTPS上的流量分析,用于增强网络安全
Woong Kyo Jung1, Byung Il Kwak1
1Division of Software, Hallym University, Chuncheon 24252, Republic of Korea.
Sensors (Basel, Switzerland)
|February 26, 2025
概括
本研究介绍了MTL-DoHTA,这是一种用于分析通过HTTPS (DoH) 流量的加密DNS的新框架. 它准确地检测恶意活动,并识别DNS道工具,增强网络安全.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 通过加密DNS查询,DNS over HTTPS (DoH) 通过加密DNS查询来提高隐私.
- 加密的DoH流量对检测恶意活动 (如DNS道) 构成挑战.
- 现有的方法很难有效地分析和分类DoH流量以达到安全目的.
研究的目的:
- 提出MTL-DoHTA,一个多任务学习框架来分析DoH流量.
- 为了分类DoH与非DoH流量,良性与恶意DoH流量,并识别特定的DNS道工具.
- 提高基于传感器的网络系统对复杂威胁的安全性.
主要方法:
- 利用来自网络流量的统计特征.
- 采用了二维卷积神经网络 (2D-CNN) 架构.
- 整合了GradNorm和注意力机制,以提高表现.
- 应用下方采样技术来处理类不平衡并减轻过度拟合.
主要成果:
- 在CIRA-CIC-DoHBrw-2020数据集上获得了0.9905的宏观平均F1得分.
- 证明有效处理类不平衡和过度装配.
- 成功分类了DoH与非DoH流量,良性与恶意DoH流量,并确定了DNS道工具.
结论:
- MTL-DoHTA提供了一种可靠的监控和保护传感器网络的方法.
- 该框架有效地检测到DoH加密流量中的复杂威胁.
- 在资源有限的环境中,MTL-DoHTA显示了增强多任务能力的潜力.
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