中文摘要：

为提出一种有效检测各类型DNS隐蔽通道的方法,研究了DNS隐蔽通信流量特性,提取可区分合法查询与隐蔽通信的12个数据分组特征,利用机器学习的分类器对其会话统计特性进行判别。实验表明,决策树模型可检测训练中全部22种DNS隐蔽通道,并可识别未经训练的新型隐蔽通道。提出的检测方法在校园网流量实际部署中成功检出了多个DNS隧道的存在。

英文摘要：

To propose an effective detection method for DNS-based covert channel, traffic characteristics were thor- oughly studied. 12 features were extracted from DNS packets to distinguish covert channels from legitimate DNS queries. Statistical characteristics of these features are used as input of the machine learning classifier. Experimental results show that the decision tree model detects all 22 covert channels used in training, and is capable of detecting untrained covert channels. Several DNS tunnels were detected during the evaluation on campus network＇s live DNS traffic.