PSO-Optimized Negative Selection Algorithm for Anomaly Detection

被引：10

作者：

Wang, Huimin ^{[1
]}

Gao, X. Z. ^{[2
]}

Huang, Xianlin ^{[1
]}

Song, Zhuoyue ^{[1
]}

机构：

[1] Harbin Inst Technol, Ctr Control Theory & Guidance Technol, Harbin 150006, Peoples R China

[2] Aalto Univ, Institute of Intelligent Power Electronics, FIN-02150 Espoo, Finland

来源：

APPLICATIONS OF SOFT COMPUTING: UPDATING THE STATE OF THE ART | 2009年 / 52卷

基金：

芬兰科学院;

关键词：

Anomaly detection; artificial immune system; negative selection algorithm; particle swarm optimization; self-nonself discrimination;

D O I：

10.1007/978-3-540-88079-0_2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, the Particle Swarm Optimization (PSO) is used to optimize the randomly generated detectors in the Negative Selection Algorithm (NSA). In our method. with a certain number of detectors. the coverage of the non-self space is maximized, while the coverage of the self samples is minimized. Simulations are performed using both synthetic and real-world data sets. Experimental results show that the proposed algorithm has remarkable advantages over the original NSA.

引用

页码：13 / +

页数：2

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