用机器学习分析DNS流量，精准识别隐蔽的恶意通信
DNS Tunneling: Threat Landscape and Improved Detection Solutions
- 融合多特征的机器学习模型分析DNS请求行为
- 检测准确率显著优于传统规则方法
- 适合网络安全团队用于威胁监测
由于DNS隧道能将恶意行为隐藏在看似正常合法的DNS流量中，检测其成为安全领域的重大挑战。传统检测方法依赖基于规则或签名匹配，往往难以准确识别此类隐蔽通信通道。本研究提出一种新型机器学习方法，通过提取DNS流量中的多种特征并结合多个算法进行分析，有效提升检测性能。实验结果表明，该方法在真实流量数据上的检测准确率显著优于现有技术，是实现高精度DNS隧道检测的有力候选方案。
原文摘要 · Abstract (English)
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.
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