| 基于分类样本和贝叶斯动态预测的异常入侵检测 |
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| 引用本文： | 付庆利.基于分类样本和贝叶斯动态预测的异常入侵检测[J].计算机工程与应用,2007,43(25):140-142. |
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| 作者姓名： | 付庆利 |
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| 作者单位： | 北京理工大学 计算机科学技术学院，北京 100081 |
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| 摘 要： | 在大规模网络环境中，入侵检测系统得到的警报数据具有一定的规律。据此提出了一种基于警报事件强度的异常检测方法，采用分类样本空间和贝叶斯动态预测方法，解决了警报数据的时间效应问题。实验数据分析表明，该方法对于大规模入侵行为具有较好的检测效果。
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| 关 键 词： | 入侵检测 警报 分类样本空间 贝叶斯动态预测 |
| 文章编号： | 1002-8331（2007）25-0140-03 |
| 修稿时间： | 2007-04 |
Anomaly intrusion detection based on classified sample and Bayesian dynamic forecast |
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| FU Qing-li.Anomaly intrusion detection based on classified sample and Bayesian dynamic forecast[J].Computer Engineering and Applications,2007,43(25):140-142. |
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| Authors: | FU Qing-li |
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| Affiliation: | School of Computer Science and Technology，Beijing Institute of Technology，Beijing 100081，China |
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| Abstract: | Alert data are in certain regulation in large network environment.An anomaly detection method based on alert data is proposed in this paper.The time impact problem has been solved by using classified sample space and Bayesian dynamic forecast method.The simple experiment shows that this method can effectively detect large scale attacks. |
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| Keywords: | intrusion detection alert data classified sample space Bayesian dynamic forecast |
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