On convergence of evolutionary negative selection algorithms for anomaly detection

Wenjian Luo; Peng Guo; Xufa Wang

doi:10.1109/CEC.2008.4631193

On convergence of evolutionary negative selection algorithms for anomaly detection

Wenjian Luo^*
, Peng Guo
, Xufa Wang

^*Corresponding author for this work

University of Science and Technology of China

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

Abstract

Evolutionary Negative Selection Algorithms (ENSAs) are proposed by combining negative selection model and evolutionary operators. In this paper, the convergence of ENSAs with two different mutation operators is analyzed. The first mutation operator is that only one bit of a detector is selected and flipped with a high probability. The second mutation operator is that every bit of a detector has a positive probability to be flipped. The analysis results show that the ENSAs with different mutation operators have different convergent properties. Especially, the shape of the self set will affect the convergence of ENSAs with the first mutation operator.

Original language	English
Title of host publication	2008 IEEE Congress on Evolutionary Computation, CEC 2008
Pages	2933-2939
Number of pages	7
DOIs	https://doi.org/10.1109/CEC.2008.4631193
State	Published - 2008
Externally published	Yes
Event	2008 IEEE Congress on Evolutionary Computation, CEC 2008 - Hong Kong, China Duration: 1 Jun 2008 → 6 Jun 2008

Publication series

Name	2008 IEEE Congress on Evolutionary Computation, CEC 2008

Conference

Conference	2008 IEEE Congress on Evolutionary Computation, CEC 2008
Country/Territory	China
City	Hong Kong
Period	1/06/08 → 6/06/08

Access to Document

10.1109/CEC.2008.4631193

Cite this

@inproceedings{d65e71abc3b34764be9812192980946c,

title = "On convergence of evolutionary negative selection algorithms for anomaly detection",

abstract = "Evolutionary Negative Selection Algorithms (ENSAs) are proposed by combining negative selection model and evolutionary operators. In this paper, the convergence of ENSAs with two different mutation operators is analyzed. The first mutation operator is that only one bit of a detector is selected and flipped with a high probability. The second mutation operator is that every bit of a detector has a positive probability to be flipped. The analysis results show that the ENSAs with different mutation operators have different convergent properties. Especially, the shape of the self set will affect the convergence of ENSAs with the first mutation operator.",

author = "Wenjian Luo and Peng Guo and Xufa Wang",

year = "2008",

doi = "10.1109/CEC.2008.4631193",

language = "英语",

isbn = "9781424418237",

series = "2008 IEEE Congress on Evolutionary Computation, CEC 2008",

pages = "2933--2939",

booktitle = "2008 IEEE Congress on Evolutionary Computation, CEC 2008",

note = "2008 IEEE Congress on Evolutionary Computation, CEC 2008 ; Conference date: 01-06-2008 Through 06-06-2008",

}

Luo, W, Guo, P & Wang, X 2008, On convergence of evolutionary negative selection algorithms for anomaly detection. in 2008 IEEE Congress on Evolutionary Computation, CEC 2008., 4631193, 2008 IEEE Congress on Evolutionary Computation, CEC 2008, pp. 2933-2939, 2008 IEEE Congress on Evolutionary Computation, CEC 2008, Hong Kong, China, 1/06/08. https://doi.org/10.1109/CEC.2008.4631193

On convergence of evolutionary negative selection algorithms for anomaly detection. / Luo, Wenjian; Guo, Peng; Wang, Xufa.
2008 IEEE Congress on Evolutionary Computation, CEC 2008. 2008. p. 2933-2939 4631193 (2008 IEEE Congress on Evolutionary Computation, CEC 2008).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - On convergence of evolutionary negative selection algorithms for anomaly detection

AU - Luo, Wenjian

AU - Guo, Peng

AU - Wang, Xufa

PY - 2008

Y1 - 2008

N2 - Evolutionary Negative Selection Algorithms (ENSAs) are proposed by combining negative selection model and evolutionary operators. In this paper, the convergence of ENSAs with two different mutation operators is analyzed. The first mutation operator is that only one bit of a detector is selected and flipped with a high probability. The second mutation operator is that every bit of a detector has a positive probability to be flipped. The analysis results show that the ENSAs with different mutation operators have different convergent properties. Especially, the shape of the self set will affect the convergence of ENSAs with the first mutation operator.

AB - Evolutionary Negative Selection Algorithms (ENSAs) are proposed by combining negative selection model and evolutionary operators. In this paper, the convergence of ENSAs with two different mutation operators is analyzed. The first mutation operator is that only one bit of a detector is selected and flipped with a high probability. The second mutation operator is that every bit of a detector has a positive probability to be flipped. The analysis results show that the ENSAs with different mutation operators have different convergent properties. Especially, the shape of the self set will affect the convergence of ENSAs with the first mutation operator.

UR - https://www.scopus.com/pages/publications/55749092255

U2 - 10.1109/CEC.2008.4631193

DO - 10.1109/CEC.2008.4631193

M3 - 会议稿件

AN - SCOPUS:55749092255

SN - 9781424418237

T3 - 2008 IEEE Congress on Evolutionary Computation, CEC 2008

SP - 2933

EP - 2939

BT - 2008 IEEE Congress on Evolutionary Computation, CEC 2008

T2 - 2008 IEEE Congress on Evolutionary Computation, CEC 2008

Y2 - 1 June 2008 through 6 June 2008

ER -

On convergence of evolutionary negative selection algorithms for anomaly detection

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this