Абстрактный

Hybrid Intrusion Detection Model Based on Clustering and Association

Manish Somani, Roshni Dubey

There are several intrusion detection model are presented till now. But there is still need of betterment in this direction. Our paper focuses on the limitation faced in the traditional approach. In this paper we suggest a hybrid framework based on clustering and association. Clustering is used for separate it on the basis of various classes and on the bases of classes we can classify it. FP growth algorithm is then used as the association classifier which can classify the data accordingly on the same set of category which can be proof to be better in terms of intrusion detection.