吉林大学学报(工学版) ›› 2012, Vol. 42 ›› Issue (01): 176-181.

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Real-value negative selection algorithm for anomaly detection

CHAI Zheng-yi1,2, WANG Xian-rong1, WANG Liang3   

  1. 1. School of Information Science and Engineering, He'nan University of Technology, Zhengzhou 450001,China;
    2. School of Computer Science and Technology,Xidian University, Xi'an 710071,China;
    3. Department of Materials Management, Chengde Medical University, Chengde 067000,China
  • Received:2010-03-24 Online:2012-01-01 Published:2012-01-01

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Abstract:

An optimized detector generation algorithm was proposed to solve the shortcomings of existing real-value negative selection algorithm. This algorithm fully utilizes self distribution, optimizes the center of the detector and expands the radius of the detector in order to generate a detector with large coverage. The expected coverage was used as one of the control parameters to end the algorithm so that it can effectively avoid the generation of redundant detector. Moreover, the anomaly detection system and it formal description were established. The error rate as a new index for anomaly detection performance was defined. The 2DSyntheticData and the actual Irish data set and biomedical data were used to test the algorithm. Experimental results show that, compared with V-detector, the algorithm improves detection rate, reduces the error rate and the required number of detectors. So it has a better detection performance.

Key words: artificial intelligence, anomaly detection, negative selection algorithm, detector, detection performance

CLC Number: 

  • TP18


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