A FDRS-Based Data Classification Method Used for Abnormal Network Intrusion Detection

The data mining techniques used for extracting patterns that represent abnormal network behavior for intrusion detection is an important research area in network security.Based on the new proposed theoretical model of recognition space and further division method, this paper introduces a novel improvement of neural network classification: further division of recognition space(FDRS).Then studied the method to classify samples by mapping what to further divided recognition space.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 network processor-based platforms.

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