摘要
入侵检测作为一种动态的安全防护技术,提供了对内部攻击、外部攻击和误操作的实时保护。作者提出了一个基于遗传神经网络的入侵检测方法,采用遗传算法和BP神经网络相结合的方法遗传神经网络应用于入侵检测系统中,解决了传统的BP算法的收敛速度慢、易陷入局部最小点的问题。研究表明,该方法效果良好,学习速度快,分类准确率高。
Intrusion detection is regarded as a dynamical security technology, and offers frequently security for inner attack, outer attack and wrong operation. It holds back and responds to intrusion before the network system lies in endanger. Based on genetic neural networks, the authors bring forward the intrusion detection method by combining genetic algorithm with BP neural networks and use it in the intrusion detection to solve the problems of the slow convergence rate and minimal immersion value of the traditional BP algorithm. The result proves that this technology is good and has the advantage of learning rapidly and high accurate classification.
出处
《成都理工大学学报（自然科学版）》
CAS
CSCD
北大核心
2005年第4期419-422,共4页
Journal of Chengdu University of Technology: Science & Technology Edition
关键词
入侵检测
遗传算法
遗传神经网络
intrusion detection
genetic algorithm（GA）
genetic neural networks（GABP）
作者简介
鲁红英（1974-），女，硕士，讲师，主要从事计算机基础教学工作．
