【作者】 鲁红英；

【导师】 孙淑霞；

【作者基本信息】 成都理工大学 ， 地球探测与信息技术， 2004， 硕士

【摘要】 随着科技进步和计算机网络技术的飞速发展，信息产业及其应用得到了巨大发展，政府、金融、电信等企事业单位及个人用户等对网络的依赖程度越来越高，同时也由此带来了信息安全的隐患，如何保障网络与信息系统的安全已经成为人们高度重视的问题。 传统的网络安全模型已经不能适应网络技术的发展，PPDR模型应运而生。入侵检测技术是PPDR模型的重要组成部分，是防火墙、数据加密等安全保护措施的有效补充。它对计算机和网络资源上的恶意使用行为进行识别，并为对抗入侵提供重要信息，它不仅检测来自外部的入侵行为，同时也监督内部用户的未授权活动。 入侵检测分析技术是入侵检测系统的核心，主要分为异常入侵检测和误用入侵检测。作者在对传统网络安全模型、PPDR模型、入侵检测原理以及常用入侵检测技术进行比较分析的基础上，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。更多还原

【Abstract】 With the development of computer network techniques and science technology, information industry and it’s use have expanded greatly, enterprise(for instance, government, finance, telegraphy .Etc) and personal user have depended on networks more and more larger, at the same time, such has brighten lots of information security in hidden trouble, network security is increasingly paid attention to and concerned about, it is critical problem how to protect security in networks and information system.Traditional network security model could not fit development of network technology, PPDR model emerged, as the times require. Instruction detection technology is PPDR model importantly composed part, and it make up for absence about firewall and data security protection. This technology has not only distinguished from computer and network resources, but also has given important information in instruction; it has not only detected instructing action from out word, but also has controlled user’s actions.Instruction detection technology is core in instruction detection system, it include abnormity instruction and abused instruction detection, on the basis of traditional network security model, PPDR model, instruction detection principle and instruction technology analysis, the author has brought forward instruction detection method basedGenetic neural networks, adopted Genetic algometry and BP neural networks union method, and applied in instruction detection system, solve traditional BP algometry lie in absence about constringency rate slowly and immersion minim value. The result proved, this technology was well, it lied in advantage about learning rate rapidity, classify nicety high.更多还原