ANOMALY DETECTION USING NEIGHBORHOOD NEGATIVE SELECTION

作者：Wang, Dawei; Xue, Yibo^*; Dong, Yingfei

来源：Intelligent Automation and Soft Computing, 2011, 17(5): 595-605.

DOI：10.1080/10798587.2011.10643173

• Anomaly Detection
• Artificial Immune
• Negative Selection
• Neighborhood

Negative Selection Algorithms (NSAs) have been widely used in anomaly detection. As the security issue becomes more complex, more and more anomaly detection schemes involve high-dimension data. NSAs however perform poorly on effectiveness and efficiency when dealing with high-dimension data. To address these issues, we propose a Neighborhood Negative Selection (NNS) algorithm in this paper. Instead of a single data point, NNS uses a neighborhood to represent a self sample (or a detector). As a result, the training efficiency is greatly improved. 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.

• 出版日期2011
• 单位深圳清华大学研究院