Vulnerability analysis of immunity-based intrusion detection systems using genetic and evolutionary hackers

被引：14

作者：

Dozier, Gerry ^{[1
]}

Brown, Douglas

Hou, Haiyu

Hurley, John

机构：

[1] Auburn Univ, Dept Comp Sci & Software Engn, Auburn, AL 36849 USA

[2] Clark Atlanta Univ, Dept Comp Sci, Atlanta, GA 30314 USA

[3] Boeing Co, Distributed Syst Integrat, Seattle, WA 98124 USA

来源：

APPLIED SOFT COMPUTING | 2007年 / 7卷 / 02期

关键词：

artificial immune systems; intrusion detection systems; GENERTIA;

D O I：

10.1016/j.asoc.2006.05.001

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Artificial immune systems ( AISs) are biologically inspired problem solvers that have been used successfully as intrusion detection systems ( IDSs). In this paper, we compare a genetic hacker with 12 evolutionary hackers based on particle swarm optimization (PSO) that have been effectively used as vulnerability analyzers ( red teams) for AIS-based IDSs. Our results show that the PSO-based red teams that use Clerc's constriction coefficient outperform those that do not. Our results also show that the three types of red teams ( genetic, basic PSO, and PSO with the constriction coefficient) have distinct search behaviors that are complimentary. (c) 2006 Elsevier B. V. All rights reserved.

引用

页码：547 / 553

页数：7

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