A negative selection algorithm with neighborhood representation

ZHANG Feng-bin^1,2, WANG Da-wei¹, WANG Sheng-wen³

1.Dept.of Computer Science & Technology,Harbin University of Science & Technology,Harbin 150080,China;2.Dept.of Computer Science & Technology,Harbin Institute of Technology,Harbin 150001,China;3.Dept.of Computer Science & Technology,Tsinghua University,Beijing 100084,China



Abstract:

This paper proposes a negative selection with neighborhood representation named as neighborhood negative selection algorithm.This algorithm employs a new representation method which uses the fully adjacent but mutually disjoint neighborhoods to present the self samples and detectors.After normalizing the normal samples into neighborhood shape space,the algorithm uses a special matching rule similar as Hamming distance to train mature detectors at the training stage and detect anomaly at the detection stage.The neighborhood negative selection algorithm is tested using KDD CUP 1999 dataset.Experimental results show that the algorithm can prevent the negative effect of the dimension of shape space,and provide a more accuracy and stable detection performance.

Key words:  artificial immune  negative selection  neighborhood  matching rule

DOI：10.11916/j.issn.1005-9113.2011.03.014

Clc Number:TP301.6

Fund: