NNS：A Novel Neighborhood Negative Selection Algorithm
As the security issue becomes more complex,more and more anomaly detection schemes involve high-dimension data. Negative selection algorithms have been widely used in anomaly detection, fault detection, and fraud detection. However, these algorithms perform poorly when dealing with high dimension data. To address this issue, we propose a novel Neighborhood Negative Selection (NNS) algorithm in this paper. In NNS, we use a neighborhood set to represent a selfsample (or a detector), instead of a single data point. As a result, the delay for training detectors is greatly reduced. We further introduce a special matching mechanism to limit the negative effect of the dimensionality of a shape space and improve the detecting performance in high dimensions. The experimental results show that NNS can provide a more accurate and stable detection performance. Meanwhile, both theoretical analysis and experimental results show that NNS further improves the training efficiency.
Anomaly detection Artificialimmune Negative selection Neighborhood
Dawei Wang YiboXue Yingfei Dong
Research Inst. of Info.& Tech.,Tsinghua University,Beijing,China Department of Electrical Engineering,University of Hawaii,Honolulu,HI 96822,USA
国际会议
宜昌
英文
453-457
2010-10-10（万方平台首次上网日期，不代表论文的发表时间）