Anomaly intrusion detection based on classified sample and Bayesian dynamic forecast

Computer Engineering and Applications ›› 2007, Vol. 43 ›› Issue (25): 140-142.

• 网络、通信与安全 • Previous Articles Next Articles

Anomaly intrusion detection based on classified sample and Bayesian dynamic forecast

FU Qing-li

School of Computer Science and Technology，Beijing Institute of Technology，Beijing 100081，China

Received:1900-01-01 Revised:1900-01-01 Online:2007-09-01 Published:2007-09-01
Contact: FU Qing-li

基于分类样本和贝叶斯动态预测的异常入侵检测

付庆利

北京理工大学计算机科学技术学院，北京 100081

通讯作者: 付庆利

Abstract

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.

Key words: intrusion detection, alert data, classified sample space, Bayesian dynamic forecast

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。

关键词: 入侵检测, 警报, 分类样本空间, 贝叶斯动态预测

FU Qing-li. Anomaly intrusion detection based on classified sample and Bayesian dynamic forecast [J]. Computer Engineering and Applications, 2007, 43(25): 140-142.

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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Anomaly intrusion detection based on classified sample and Bayesian dynamic forecast

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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