Abstract
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.
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Zeng, Y., Zhuang, J. (2010). Construction Cosine Radial Basic Function Neural Networks Based on Artificial Immune Networks. In: Cao, L., Zhong, J., Feng, Y. (eds) Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science(), vol 6441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17313-4_13
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DOI: https://doi.org/10.1007/978-3-642-17313-4_13
Publisher Name: Springer, Berlin, Heidelberg
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