| 基于灰色神经网络的入侵检测系统研究 |
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| 引用本文： | 阳树洪,李春贵,夏冬雪.基于灰色神经网络的入侵检测系统研究[J].计算机工程与设计,2007,28(19):4622-4624,4627. |
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| 作者姓名： | 阳树洪 李春贵 夏冬雪 |
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| 作者单位： | 广西工学院,计算机工程系,广西,柳州,545006;西南交通大学,信息科学与技术学院,四川,成都,631031 |
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| 基金项目： | 广西自然科学基金
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广西工学院硕士基金 |
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| 摘 要： | 将灰色预测和神经网络有机的结合起来,构造出了新的灰色神经网络GNNM,并用于入侵检测系统(IDS)中,仿真结果表明,GNNM算法在较低误报率的基础上达到了理想的检测率,与传统的神经网络算法相比,不但提高了系统的并行计算能力和系统的可用信息的利用率,还提高了系统的建模效率与模型精度.
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| 关 键 词： | 入侵检测 灰色系统 神经网络 建模 检测率 |
| 文章编号： | 1000-7024（2007）19-4622-03 |
| 修稿时间： | 2006-10-30 |
Study on intrusion detection system based on grey neural networks |
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| YANG Shu-hong,LI Chun-gui,XIA Dong-xue.Study on intrusion detection system based on grey neural networks[J].Computer Engineering and Design,2007,28(19):4622-4624,4627. |
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| Authors: | YANG Shu-hong LI Chun-gui XIA Dong-xue |
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| Affiliation: | 1. Department of Computer Engineering, Guangxi University of Technology, Liuzhou 545006, China; 2. College of Information Science and Technology, Southwest Jiaotong University, Chengdu 631031, China |
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| Abstract: | Grey prediction is integrated with neural network and thus a new version of grey neural network model(GNNM) is constructed,then GNNM is applied to intrusion detection system.The simulation shows that GNNM algorithm reaches an ideal intrusion detection rate on the basis of low misinformation rate.Compared with traditional neural network algorithm,GNNM not only increases the parallel computing power of the system and the utilization of available information,but also improves the efficiency and refinement of model constructing. |
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| Keywords: | intrusion detection grey system neural network modeling detection rate |
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