Intrusion Detection Model Based on Hierarchical Fuzzy Inference System

With the growing rate of network attacks, intelligent methods for detecting new attacks have attracted increasing interest. This paper presents an approach incorporating several soft computing techniques to construct a hierarchical neuro-fuzzy inference intrusion detection system which can implement either misuse or anomaly detection. In the proposed system principal component analysis neural network is used to reduce the dimensions of the feature space. And the preprocessed data is clustered by applying an enhanced fuzzy c-means clustering algorithm to extract and manage fuzzy rules. The system developments two level neuro-fuzzy inference system. Genetic algorithm is used to optimize the structure of the system. Finally a publicly available DRAPA/KDD99 dataset is used to demonstrate the approaches and the results show their accuracy.