Real-Valued Negative Selection Algorithm with Variable-Sized Self Radius

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• 作者：Jinquan Zeng (12) zengjq@uestc.edu.cn
  Weiwen Tang (2)
  Caiming Liu (3)
  Jianbin Hu (4)
  Lingxi Peng (5)
• 关键词：Artificial Immune Systems &#8211 ; Negative Selection Algorithm &#8211 ; Anomaly Detection
• 刊名：Lecture Notes in Computer Science
• 出版年：2012
• 出版时间：2012
• 年：2012
• 卷：7473
• 期：1
• 页码：229-235
• 全文大小：202.8 KB
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• 作者单位：1. School of Computer Science & Engineering, University of Electronic Science and Technology of China, 610054 Chengdu, China2. Sichuan Communication Research Planning & Designing Co., Ltd, 610041 Chengdu, China3. Laboratory of Intelligent Information Processing and Application, Leshan Normal University, 614004 Leshan, China4. School of Electronics & Information, Nantong University, 226019 Nantong, China5. Department of Computer and Education Software, Guangzhou University, 510006 Guangzhou, China
• ISSN：1611-3349

文摘

Negative selection algorithm (NSA) generates the detectors based on the self space. Due to the drawbacks of the current representation of the self space in NSAs, the generated detectors cannot enough cover the non-self space and at the same time, cover some of the self space. In order to overcome the drawbacks, a new scheme of the representation of the self space is introduced with variable-sized self radius, which is called VSRNSA. Using the variable-sized self radius to represent the self space, we can generate the more quality detectors. The algorithm is tested using the well-known real world datasets; preliminary results show that the new approach enhances NSAs in increasing detection rates and decrease false alarm rates, and without increase in complexity.