Increasing time-spent online has amplified users' exposure to the threat of information leakage.Although existing security systems(such as firewalls and intrusion detection systems) can satisfy most of the security requirements of network administrators,they are not suitable for detecting the activities of applying the HTTP-tunnel technique to steal users' private information.This paper focuses on a network behavior-based method to address the limitations of the existing protection systems.At first,it analyzes the normal network behavior pattern over HTTP traffic and select four features.Then,it presents an anomaly-based detection model that applies a hierarchical clustering technique and a scoring mechanism.It also uses real-world data to validate that the selected features are useful.The experiments have demonstrated that the model could achieve over 93%hit-rate with only about 3%falsepositive rate.It is regarded confidently that the approach is a complementary technique to the existing security systems.
针对DNS可解析性量化评估问题,提出了基于命题逻辑的DNS可解析性量化评估方法。该方法通过对域名解析依赖关系的分析建立域名解析依赖关系图,结合命题逻辑将域名对DNS服务器的依赖性转换为逻辑图,进而将域名的可解析性问题转换为数理逻辑中的命题公式的可满足性问题;通过对命题公式进行析取范式的转换和对命题变项数最少的简单合取范式的分析,获取域名可解析的最小DNS服务器组合,从而指导DNS服务器的布置和规划;通过对命题公式进行合取范式的转换和对命题变项数最少的简单析取范式的分析,获取域名不可解析的最小DNS服务器组合,从而对这些DNS服务器进行重点防护。以Alex Top 1000域名为例分析了解析依赖关系,经统计计算发现,Alex Top 1000域名解析平均依赖DNS数量为5.58个;域名解析依赖的最小服务器组合在[1,4]区间内,平均值为1.41个;破坏域名解析的最小服务器组合在[1,6]区间内,平均值为2.44个。
