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

• Published in: Information Computing and Applications pp. 229 - 235
• Main Authors: Zeng, Jinquan, Tang, Weiwen, Liu, Caiming, Hu, Jianbin, Peng, Lingxi
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2012
• Series: Lecture Notes in Computer Science
• Subjects: Anomaly Detection; Artificial Immune Systems; Negative Selection Algorithm
• ISBN: 9783642340611; 364234061X
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-642-34062-8_30

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.