期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2006
卷号：6
期号：2B
页码：173-173~177
出版社：International Journal of Computer Science and Network Security
摘要：Intrusion detection technology is an effective approach to dealing with the problems of network security. In this paper, we 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, our 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. We employ DARPA intrusion detection evaluation data set to train and test the feasibility of our proposed method. Experimental results show that the performance is efficient and satisfactory. Finally, the development trend of intrusion detection technology and its currently existing problems are briefly concluded.
