Research Of Key Technologies To Monitor And Analyze The BBS Information Transferred Through Backbone Network

With rapid development in last twenty years, Internet, originally a convenient communication tool, has been evolved into a virtual society. BBS is one of the important communities of this virtual society. Widely and quickly spreading of BBS consensus, guides the public opinion to some extent. To monitor and analyze the BBS information is an effective way to know the public opinion situation. It can play an important role to govern the virtual society. This dissertation studies some key technologies to monitor and analyze BBS information transferred through backbone networks. The main contributions are as follows:1. Put forward a "Layered and Distributed System Architecture Model to Monitor and Analyze the Communication Information Transferred Through High Speed Backbone Networks". The model divides the layers by technical functionalities and features, and composes distributed monitoring nodes according to regional backbone administration. The key processes of Data Capture, Information Extraction, Information Storing,Information Deep Analyzing, coordinated Monitoring Applications are concluded and abstracted. Among those, Data Capture, Information Extraction, and Deep Analyzing technologies are described in follow-up chapters separately.2. A "Filtering and Distributing Device based on Logical Output Port Group" is designed. It can duplicate and filter data packets flexibly. It can allocate packets-flow to different logic ports in a group, and connect to external switches for second-level distribution. Based on that, a "Forward-Caching with Dynamic-Feedback Filtering and Distributing Mechanism" is suggested for optimization. It realizes session level data contents filtering, and flexible related-packets capture. The new mechanism can notably reduce the number of the devices deployed.3. A "Method to Extract BBS Information from Captured Packets by SVM and Layered CRF Technology" is proposed. It automatically recognizes the BBS sites by analyzing macro-features of captured packets with SVM. It adopts layered CRF technology to determine the behavior-type of BBS sessions, labels the elements-type, and composes wrappers for information extraction. Then it fulfills automatic extraction of BBS information transferred through backbone networks by wrapper technology.4. A "BBS consensus Characteristic Parameter Structure based on Captured Information" is defined. A "Deep Analyzing Method based on the BBS Consensus Characteristic Parameters " is proposed. They take into account the relationship among BBS sites, board, netizen and post-notes. They integrate the key elements together such as the interests and involvement netizen showed towards a specific consensus, the spread-speed, the attention paid to BBS sites, etc. By utilizing the characteristics and analyzing the key elements extracted from the captured-data, we can obtain the regular characteristics, evaluate the situation and predict the trends of BBS consensus.