Cloud Intrusion Detection System Using Fuzzy Clustering and Artificial Neural Network

Abstract

Intrusion detection system (IDS) is a security layer used to detect suspicious activities and generate alerts when such activities are identified in systems. Artificial Neural Networks (ANN) can be used to detect the intrusion in the system but there is small problem that ANN lacks in certain areas that are detection precision for low frequent attacks and detection stability. So we have decided to implement FC-ANN approach based on fuzzy clusters and artificial neural network, to solve the problem. The general procedure of FC-ANN is as follows: initially fuzzy clustering technique is used to generate different training subsets. Subsequently, based on different training subsets, different ANN models are trained to make different base models. Finally, a meta-learner, fuzzy aggregation module, is engaged to combine these results. In addition to this we are going to add restore point which allows for the registry keys, rolling back of system files, installed programs and the project data base etc.