作者信息
- 1. 中南财经政法大学信息与安全工程学院 430073
- 折叠
摘要
本文利用神经网络的强大的自学习能力和自适应性来提高安全审计系统的性能。在众多神经网络中，PNN概率神经网络尤为适合运用到入侵行为模式的误用检测方面。因此本文将PNN概率神经网络和入侵检测技术结合起来，研究保护企业数据库的安全技术，并构造出一套企业数据库入侵误用检测模型。该模型主要用于检测已知的入侵行为模式，并给系统及时处理入侵行为提供依据。
Abstract
The thesis uses the neural network's powerful self- learning and self- adaptive to improve the performance of the security audit system. In many neural networks, the Probabilistic Neural Network is particularly suitable for the misuse detection of intrusion mode. The thesis combines the method of the Probabilistic Neural Network with the technique of intrusion detection, and constructs a model of intru-sion detection system applied to the corporation database. The mode can detect the known intrusion modes, in the same time, provides the basis for system to deal with the intrusions in time.
关键词
数据库安全/PNN概率神经网络/入侵检测Key words
Database security/Probabilistic Neural Network/Intrusion detection引用本文复制引用
出版年
2014
NETL
NSTL
万方数据