A HTTP Session Learning Model based on DFA
Jin Ma · Information Security and Communications Privacy · 2010
The protection of Web server-based applications by using intrusion detection, for their large, complex structure are faced with severe test. Intrusion detection with learning function has the potential to improve the state of affair. This paper describes how HTTP sessions are extracted from HTTP connections, and how DFA is introduced to build a model for HTTP sessions according to HTTP requests in RFC format. For the large size of HTTP requests, a algorithm for model simplification is proposed. The model maintains the feature of automatic updating, and this could serve as a strategy to meet the requirements for protecting Web applications.