标题： DNS隧道：威胁环境和改进的检测解决方案 显示英文标题

标题： DNS Tunneling: Threat Landscape and Improved Detection Solutions

摘要： 检测域名系统（DNS）隧道在安全领域是一个重要的挑战，因为其能够将有害行为隐藏在看似正常和合法的DNS流量中。 传统的检测方法基于规则的方法或签名匹配的方法，这些方法通常不足以准确识别此类隐蔽的通信通道。 本研究旨在有效检测DNS隧道。 我们提出了一种新的方法，利用机器学习算法来检测DNS隧道。 我们将机器学习算法结合使用，通过从DNS流量中提取特征来分析流量。 分析结果表明，所提出的方法是准确检测DNS隧道的良好候选方案。 显示英文摘要

摘要： Detecting Domain Name System (DNS) tunneling is a significant challenge in security due to its capacity to hide harmful actions within DNS traffic that appears to be normal and legitimate. Traditional detection methods are based on rule-based approaches or signature matching methods that are often insufficient to accurately identify such covert communication channels. This research is about effectively detecting DNS tunneling. We propose a novel approach to detect DNS tunneling with machine learning algorithms. We combine machine learning algorithms to analyze the traffic by using features extracted from DNS traffic. Analyses results show that the proposed approach is a good candidate to detect DNS tunneling accurately.