Due to the growth of rule base usage, the scale of rule base is increasing and hence many management related issues arise about constructing the rule base. Three are issues about rule base maintenance, including complex relationships between rules, structural errors, and performance degradation. In this thesis, RP-MES is proposed to solve these issues by designing a new approach combining both rule base partitioning mechanism and meta-rule construction mechanism. As for rule base partitioning, RP-MES not only takes care of the structural relatedness between rules, but also considers the semantic relatedness of rules by calculating the semantic relationship between rules in the rule base. On the other hand, RP-MES extracts the meta-rules from the rule clusters partitioned by the rule base partitioning mechanism, and uses the obtained meta-rules for selecting rule cluster when using the rule base.
In order to evaluate the performance of RP-MES, an Intrusion Detection System (IDS) prototype is also designed and implemented based on RP-MES, and some experiments have been done to test the system performance. The experimental results show that RP-MES can produce reasonable number of rule clusters and the accuracy of the inference result remains, and that the performance of RP-MES is better than that of original rule base without partitioning.
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