An Approach of Data Mining Techniques Using Firewall Detection for Security and Event Management System

Abstract

Security is one of the most important issues to force a lot of research and development effort in last decades. We are introduced a mining technique like firewall detection and frequent item set selection to enhance the system security in event management system. In addition, we are increasing the deduction techniques we have try to overcome attackers in data mining rules using our SIEM project. In proposed work to leverages to significantly improve attack detection and mitigate attack consequences. And also we proposed approach in an advanced decision-making system that supports domain expert’s targeted events based on the individuality of the exposed IWIs. Furthermore, the application of different aggregation functions besides minimum and maximum of the item sets. Frequent and infrequent weighted item sets represent correlations frequently holding the data in which items may weight differently. However, we need is discovering the rare or frequent data correlations, cost function would get minimized using data mining techniques. There are many issues discovering rare data like processing the larger data, it takes more for process. Not applicable to discovering data like minimum of certain values. We need to handle the issue of discovering rare and weighted item sets, the frequent weighted itemset (WI) mining problem. Two novel quality measures are proposed to drive the WI mining process and Minimal WI mining efficiently in SIEM system