入侵检测系统的反向传播神经网络方法

I. Mukhopadhyay, M. Chakraborty, S. Chakrabarti, Tanusree Chatterjee
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引用次数: 30

摘要

随着互联网的发展,网络的脆弱性也随之增加。如今,公司花费大量资金来保护他们的敏感数据免受各种攻击。本文提出了一种基于反向传播神经网络(BPN)模型的入侵检测系统开发方法。利用基准入侵KDDCUP’99数据集,利用Matlab2010a对系统进行了仿真,验证了系统的可行性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Back propagation neural network approach to Intrusion Detection System
As the Internet is growing - so is the vulnerability of the network. Companies now days are spending huge amount of money to protect their sensitive data from different attacks that they face. In this paper, we propose a new methodology towards developing an Intrusion Detection System (IDS) based on Back-Propagation Neural Network (BPN) model. The proposed system was simulated using Matlab2010a utilizing benchmark intrusion KDDCUP'99 dataset to verify its feasibility and effectiveness.
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