文献类型：期刊文献

中文题名：粗糙集在基于神经网络的入侵检测系统的探讨

英文题名：Research of intrusion detection model based on rough set and neural network

作者：于成[1]

第一作者：于成

机构：[1]定西师范高等专科学校,甘肃定西743000

第一机构：甘肃中医药大学定西校区

年份：2010

期号：5

起止页码：129

中文期刊名：自动化与仪器仪表

外文期刊名：Automation & Instrumentation

收录：CSTPCD

语种：中文

中文关键词：入侵检测;粗糙集;LVQ神经网络

外文关键词：Intrusion detection ; Rough sets ; LVQ neural network

摘要：随着计算机网络的发展,传统的计算机安全理论己不能适应网络环境的发展变化。传统的入侵检测系统在有效性、适应性和可扩展性方面存在一定的不足。因此,神经网络、遗传算法、粗糙集等理论被不断引入到入侵检测领域,来提高入侵检测的性能。本文主要是在对提高入侵检测能力的有效手段方面作了一些探讨。
The fact that the traditional theory of computer security has been unable to meet the development of the network environment changes. Tradition The effectiveness of intrusion detection systems, adaptability and scalability there are some deficiencies. Therefore, God The networks, genetic algorithms, rough sets and other theories have been continuously introduced into the field of intrusion detection, to improve intrusion detection Measuring performance. This paper is to improve the effective means of intrusion detection capabilities has done some research.