论文部分内容阅读
入侵检测技术是解决网络安全的一种有效手段。文中提供一个基于规则和神经网络系统的入侵检测模型。主要思想是利用神经网络的分类能力来识别未知攻击,使用基于规则系统识别已知攻击。神经网络对DOS和Probing攻击有较高的识别率,而基于规则系统对R2L和U2R攻击检测更有效。因此该模型能提高对各种攻击的检出率。最后对模型存在的问题及入侵检测技术的发展趋势做了讨论。
Intrusion detection technology is an effective way to solve network security. The article provides a rule-based and neural network intrusion detection model. The main idea is to use the neural network’s classification ability to identify unknown attacks and to use rule-based systems to identify known attacks. Neural networks have high recognition rates for DOS and Probing attacks, while rule-based systems are more effective for R2L and U2R attacks. Therefore, the model can improve the detection rate of various attacks. Finally, the problems existing in the model and the trend of intrusion detection technology are discussed.