一个两层马尔可夫链异常入侵检测模型

在现有的单层马尔科夫链异常检测模型基础上,提出一种崭新的两层模型.将性质上有较大差异的两个过程,不同的请求和同一请求内的系统调用序列,分为两层,分别用不同的马尔可夫链来处理.两层结构可以更准确地刻画被保护服务进程的动态行为,因而能较大地提高异常的识别率,降低误警报率.而且异常检测出的异常将被限制在相应的异常真正发生的请求区内.检测模型适合于针对特权进程(特别是基于请求?反应型的特权进程)的异常入侵检测.

A Two-Layer Markov Chain Anomaly Detection Model

On the basis of the current single layer Markov chain anomaly detection model, this paper proposes a new two-layer model. Two distinctly different processes, the different requests and the system call sequence in the same request section, are classified as two layers and dealt with by different Markov chains respectively. The two-layer frame can depict the dynamic activity of the protected process more exactly than the single layer frame, so that the two-layer detection model can promote the detection rate and degrade the false alarm rate. Furthermore, the detected anomaly will be limited in the corresponding request sections where anomaly happens. The new detection model is suitable for privileged processes, especially for those based on request-response.