Construction Cosine Radial Basic Function Neural Networks Based on Artificial Immune Networks

摘要：

In this paper, we propose a novel Intrusion Detection algorithm utilizing both Artificial Immune Network and RBF neural network. The proposed anomaly detection method using multiple granularities artificial immune network algorithm to get the candidate hidden neurons firstly, and then, we training a cosine RBF neural network base on gradient descent learning process. The principle interest of this work is to benchmark the performance of the proposed algorithm by using KDD Cup 99 Data Set, the benchmark dataset used by IDS researchers. It is observed that the proposed approach gives better performance over some traditional approaches.

关键词： Intrusion Detection Algorithm RBF Neural Network Multiple Granularities Immune Network

作者: YongJin Zeng JianDong Zhuang

作者单位: College of Computer Science and Technology, Jimei University, 361021, XiaMen, China

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 134-141

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）