A DGC-based data classification method used for abnormal network intrusion detection

Authors:
Bo Yang;Lizhi Peng;Yuehui Chen;Hanxing Liu;Runzhang Yuan
Affiliations:
State Key Lab. of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Science and Technology, China and School of Information Science and Technology, Jinan University, ...;School of Information Science and Technology, Jinan University, Jinan, China;School of Information Science and Technology, Jinan University, Jinan, China;State Key Lab. of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Science and Technology, China;State Key Lab. of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Science and Technology, China
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Year:
2006

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Abstract

The data mining techniques used for extracting patterns that represent abnormal network behavior for intrusion detection is an important research area in network security. This paper introduces the concept of gravitation and gravitation field into data classification by utilizing analogical inference, and studied the method to calculate data gravitation. Based on the theoretical model of data gravitation and data gravitation field, the paper presented a new classification model called Data Gravitation based Classifier (DGC). The proposed approach was applied to an Intrusion Detection System (IDS) with 41 inputs (features). Experimental results show that the proposed method was efficient in data classification and suitable for abnormal detection using netowrk processor-based platforms.