Data Mining for Network Intrusion Detection System in Real Time

Intrusion detection technology is an effective approach to dealing with the problems of network security. This paper present a data mining-based Network Intrusion Detection framework in real time (NIDS). This framework is a distributed architecture consisting of sensor, data preprocessor, extractors of features and detectors. To improve efficiency, the approach adopts a novel FP-tree structure and FP-growth mining method to extract features based on FP-tree without candidate generation. FP-growth is just accord with the system of real-time and updating data frequently as NIDS. It employs DARPA intrusion detection evaluation data set to train and test the feasibility of the proposed method. Experimental results show that the performance is efficient and satisfactory.