【作者】 颜辉；

【导师】 曾大可；

【作者基本信息】 长春理工大学 ， 计算机应用技术， 2004， 硕士

【摘要】 本文是在天涯科技公司入侵检测系统算法设计的基础上完成的。主要研究人工神经网络非线性数学模型和计算方法的计算机程序实现，为实现复杂入侵检测信息的非线性整合处理提供技术支持。 入侵检测工作属于分类识别问题，其中存在着大量的非线性问题，对入侵检测资料进行综合分析与分类，准确判别恶意访问和正常访问等都涉及到多资料的整合处理，因此研制理想的非线性整合处理方法是非常必要的，而神经网络由于其内在的对不确定性的学习与适应能力，恰好能满足入侵检测工作分类识别的需求，另外神经网络的非线性映射能力也是保证其成功实现各种简单或复杂分类的主要原因，它将信息分布式存储于连结权系数中，使网络具有较高的容错性和鲁棒性。所以越来越多的人把研究工作的重点纷纷转向基于人工神经网络的非线性数学模型，以期更好地解决入侵检测中的准确分类问题。 目前，这方面的研究已有了部分成果，但大多只局限于应用一种固定的网络模型来解决某一具体的应用，还远远不能满足入侵检测漏报和误报率高的问题，本文的研究目的就是要建立一种灵活的网络模型，并在算法实现过程中使其具有通用性，以满足各种应用的需要，尤其是使入侵检测的漏报率和误报率趋于最小化。 本文最后把改进BP算法应用于入侵检测系统中使专家系统的知识精化，大大降低了误报率并且论述了用Visual C++实现该BP网络的过程。更多还原

【Abstract】 This article was completed based on the IDS algorithm design of TYKJ CO. , LTD. It mainly investigated the computer programming realization of the nonlinear mathematic model and the algorithm of the Artifical Neural Network, and thus provided technical support to the nonlinear conformity disposal of the complex IDS information.There are lots of nonlinear problems in IDS working. The integrated analyzing and sorting toward IDS data, the accurate forecasting of the intruders and common visitors etc, all come down to the multiple data conformity disposal. Therefore, it is vitally important to develop a perfect nonlinear conformity disposal method. However, some favorable characters of ANN can content these IDS working demands perfectly,in additon,the capability of nonlinear mapping of ANN also make sure that the system can execute simple or complex classification and save the information into the coefficent of connecting power,maintain the network robust and fault toleration .so more and more people alter one after another their working focus to the nonlinear mathematic model based on the ANN in order to resolve the complex problems in IDS system much better.This kind of investigations have acquired some accomplishments at present, but much of these accomplishments were limited to resolve certain frondose application using certain fixed network model and were far from satisfying the minimizing forecasting faults demands of IDS improving. The purpose of this theme is to establish a multi-net-model and make it generally applicable during the arithmetic realization process to content all application demands and thus minimize forecasting faultsIn the end of this article applying the advanced BP network to the ES in IDS minimized the false forecasting, Further more it discussed the process of using Visual C++ to realize these networks.更多还原