A Network Intrusion Detection Model Based on Artificial Immune

Abstract:

For the existing artificial immune systems applied to network intrusion detection have some shortages, an improved network intrusion detection model based on the dynamic clone selection algorithm which was put forward by Kim is proposed. The model introduces the concept of self group, which is obtained by the clustering algorithm AiNet and represents common features of normal data. The self group deals with network data before they are tested by detectors. In addition, the model adopts a design of distributed network intrusion detection, and a central server manages all the immune cells, receives vaccines and vaccinats the whole network detection hosts. Experimental results show that the number of selves and detectors are reduced, the process of affinity maturation for the detector population is speeded up, and the model achieves higher detection rate and lower false positive rate with the evolution generation increases.

• Periodical: Advanced Materials Research (Volumes 361-363)
• Pages: 687-690
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.361-363.687
• Online since: October 2011
• Authors: Xin Xiao, Rui Rui Zhang
• Keywords: Anomaly Detection, Artificial Immune, Dynamic Clonal Selection, Network Intrusion Detection, Network Security

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