New Model of Learning Web Application Firewall
This paper presents a new model of learning WAF( Web Application Firewall) to solve the problem of various attacks and the complexity of black list. By collecting,summarizing and inducting the characteristics of w eb parameters,and then matching the user input w ith it,the model could ensure the security of Web applications. This model classifies the Web page parameters as fixedtype parameter,enumeration parameter and user-input parameter,and has tw o modules,initial learning module and continuous learning module. The first one creates initial rule base according to the reasonable user input sample,then the latter one could continuously learn the characteristics of Web page based on the recently user input data,in order to adapt to application updates and the change of user needs. The model also introduces a comprehensive method of exceptional data flow statistic and the analysis of data similarity to learn selectively to resist the attacks w hich target the learning process. Experimental results has indicated that the new learning WAF has a great ability of protecting the security and learning.