A Negative Selection Algorithm Based on Hierarchical Clustering of Self Set and its Application in Anomaly Detection
- 10.2991/ijcis.2011.4.4.1How to use a DOI?
- Artificial Immune System, Negative Selection, Detector, Cluster
A negative selection algorithm based on the hierarchical clustering of self set HC-RNSA is introduced in this paper. Several strategies are applied to improve the algorithm performance. First, the self data set is replaced by the self cluster centers to compare with the detector candidates in each cluster level. As the number of self clusters is much less than the self set size, the detector generation efficiency is improved. Second, during the detector generation process, the detector candidates are restricted to the lower coverage space to reduce detector redundancy. In the article, the problem that the distances between antigens coverage to a constant value in the high dimensional space is analyzed, accordingly the Principle Component Analysis (PCA) method is used to reduce the data dimension, and the fractional distance function is employed to enhance the distinctiveness between the self and non-self antigens. The detector generation procedure is terminated when the expected non-self coverage is reached. The theory analysis and experimental results demonstrate that the detection rate of HC-RNSA is higher than that of the traditional negative selection algorithms while the false alarm rate and time cost are reduced.
- © 2011, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Wen Chen AU - Xiao-Jie Liu AU - Tao Li AU - Yuan-Quan Shi AU - Xu-Fei Zheng AU - Hui Zhao PY - 2011 DA - 2011/06/01 TI - A Negative Selection Algorithm Based on Hierarchical Clustering of Self Set and its Application in Anomaly Detection JO - International Journal of Computational Intelligence Systems SP - 410 EP - 419 VL - 4 IS - 4 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.2011.4.4.1 DO - 10.2991/ijcis.2011.4.4.1 ID - Chen2011 ER -