一种基于混沌理论的改进否定选择算法  

An Improved Negative Selection Algorithm based on Chaos Theory

作　　者：包晖[1] 

机构地区：[1]河南工业大学国际教育学院,河南郑州450001

出　　处：《计算机安全》2013年第8期74-76,共3页Network & Computer Security

基　　金：863计划(2012AA101608);河南省教育厅科技项目13A520179

摘　　要：随着网络应用规模的不断扩大,网络安全隐患越来越突出地显现出来,生物免疫系统与木马检测系统在功能上有许多相似之处,具有高度并行性、自治性、动态特征和记忆性等特点。为了能准确、及时地检测出木马入侵,解决检测系统检测正确率低、漏报率高和缺乏检测未知木马等问题,人工免疫原理已被应用到木马检测的研究中。研究了免疫系统的混沌特性,运用混沌理论对否定选择算法进行改进。实验结果显示,该算法能有效提高系统动态学习能力,增强了系统的灵敏性。With the continuously expansion of network application fields,Network security risks is getting increasingly obvious.Biological immune system and Trojan detection systems have many similarities in function,with the chatacteristics such as high degree of parallelism,autonomy,dynamic features and memorability.In order to accurately and timely detect Trojan intrusion and solve the problems such as detection systems’low accuracy rate,high non-response rates and the lack of detecting unknown Trojans,artificial immune principle has been applied to the study of Trojan detection.This paper studied the chaotic characteristics of the immune system and applied chaotic theory to improve the negative selection algorithm.The Experimental result shows that algorithm could effectively improve the system dynamic learning ability,and the sensitivity of the system could be enhanced.

关 键 词：木马检测 混沌理论 否定选择 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]