An Algorithm of Identification Learning and Optimization for Detectors

CHI Jing; YANG Zhen-yu; ZHANG Ting

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An Algorithm of Identification Learning and Optimization for Detectors

更新时间：2026-09-04

- An Algorithm of Identification Learning and Optimization for Detectors
- Vol. 30, Issue 8, Pages: 19-22(2013)
- 作者机构：
  
  河北工程大学机构 , 信息与电气工程学院,河北,邯郸,056038
- 作者简介：
- 基金信息：
- DOI：
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- Published：2013
- 稿件说明：
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池静, 杨振宇, 张婷. 一种检测器的标识学习和优化算法[J]. 微电子学与计算机, 2013, 30(8): 19-22.

CHI Jing, YANG Zhen-yu, ZHANG Ting. An Algorithm of Identification Learning and Optimization for Detectors[J]. 2013, 30(8): 19-22.
池静, 杨振宇, 张婷. 一种检测器的标识学习和优化算法[J]. 微电子学与计算机, 2013, 30(8): 19-22. DOI：

CHI Jing, YANG Zhen-yu, ZHANG Ting. An Algorithm of Identification Learning and Optimization for Detectors[J]. 2013, 30(8): 19-22. DOI：

摘要

为了检测出入侵检测中异常数据的类型以及解决成熟检测器的聚类问题，提出一种检测器的标识学习和优化算法。该算法首先对初始化的成熟检测器，以每个非己抗原为中心进行聚类学习，得到每个类内检测器的标识，用以识别异常数据的类型；然后在经过 N 代之后，对成熟检测器集中的重叠检测器进行变异和删除操作，来提高在非己空间的覆盖率。实验结果表明，该算法在提高检测器性能的同时，能够检测出异常数据的类型。

Abstract

In order to detect the types of abnormal database in intrusion detection and to solve the clustering problem of mature detectors

an algorithm of identification learning and optimization for detectors is proposed.Firstly

each initialization mature detector is clustered as the center of non-self antigens to obtain the label of every detector in class

which identifies the types of abnormal database.Then

variation and deleting are operated on the overlapping detectors which are in the set of mature detectors to improve the coverage of non-self space after N-generation. Experimental results show that the proposed algorithm is able to improve the detection performance of the detectors as well as indicating the type of abnormal database.

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