An Alert Data Mining Framework for Intrusion Detection System

Abstract

In this paper, we proposed a data mining framework for the management of alerts in order to improve the performance of the intrusion detection systems. The proposed alert data mining framework performs alert correlation analysis by using mining tasks such as axis-based association rule, axis-based frequent episodes and order-based clustering. It also provides the capability of classify false alarms in order to reduce false alarms. We also analyzed the characteristics of the proposed system through the implementation and evaluation of the proposed system. The proposed alert data mining framework performs not only the alert correlation analysis but also the false alarm classification. The alert data mining framework can find out the unknown patterns of the alerts. It also can be applied to predict attacks in progress and to understand logical steps and strategies behind series of attacks using sequences of clusters and to classify false alerts from intrusion detection system. The final rules that were generated by alert data mining framework can be used to the real time response of the intrusion detection system.

Incremental Weighted Mining based on RFM Analysis for Recommending Prediction in u-Commerce

“…The performance of proposing system was improved more counts of support and rule than the original system, it was especially worthy of notice, in the rule counts, had an effect about 4 times what the original system did before. As a result, it was efficient for us to recommend the items of association because it is strong cohesion of the attribute of item based association rules [11] with the weight based on the quantity of purchased data aggregated from the whole data with the item RFM score.…”