Application Of Fuzzy Association Mining In Intrusion Detection

Intrusion Detection is an important security technology of protecting information systems as well as firewall and data encryption techniques. It has great capabilities in identifying the malicious behaviors in networks and responding to them. The research of Intrusion Detection System is the basis of research of network security.The data mining technology is the process of picking up the useful and meaningful information and knowledge from a great deal of data with the computer technology. Association rule mining which is a method of data mining reveals the latent information and knowledge. According to the difference of the attribute values, the association rules are divided into the Boolean Association Rules and Quantitative Association Rules. The general approach of quantitative association rules mining is to map the attribute values into several intervals which will make the boundaries of intervals hard. It will increase the information loss. Based on Fuzzy Set theory, the attribute values are mapped into fuzzy intervals so that the transition of intervals is smoother and it reduces the information loss.Based on the theory of fuzzy association rules mining and Anomaly Intrusion Detection, improving the algorithm for mining the fuzzy association rules, the thesis designs an Intrusion Detection System based on fuzzy association rule mining. First in the normal situation, the standard rule set makes up of the fuzzy association rules which are picked up from the network data. Then we apply the concept of similarity and compare the rule set which is picked up in the present situation with the standard rule set according to the similarity between rule sets. According to the different similarities, the user is warned and suggested to take the proper measure for avoiding the intrusion.