Anomaly Based Intrusion Detection System Using Artificial Neural Network and Fuzzy Clustering

An intrusion detection system (IDS) is a security layer used to detect ongoing intrusive activities in information systems. Artificial Neural Networks (ANN) can be used to detect the intrusion in the system but there is slight complication 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 ANN and fuzzy clustering, to solve the problem. The general procedure of FC-ANN is as follows: firstly fuzzy clustering technique is used to generate different training subsets. Subsequently, based on different training subsets, different ANN models are trained to formulate different base models. Finally, a meta-learner, fuzzy aggregation module, is employed to aggregate these results. In addition to this we are going to add restore point which allows for the rolling back of system files, registry keys, installed programs and the project data base etc.