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.
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Yang, B., Peng, L., Chen, Y., Liu, H., Yuan, R. (2006). A DGC-Based Data Classification Method Used for Abnormal Network Intrusion Detection. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_24
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DOI: https://doi.org/10.1007/11893295_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
Online ISBN: 978-3-540-46485-3
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